Detecting neoplasm

ABSTRACT

This document relates to methods and materials for detecting premalignant and malignant neoplasms. For example, methods and materials for determining whether or not a stool sample from a mammal contains nucleic acid markers or polypeptide markers of a neoplasm are provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/827,013, filed Aug. 14, 2015, which is a continuation of U.S. patentapplication Ser. No. 14/168,552, filed Jan. 30, 2014, now U.S. Pat. No.9,121,070, which is a continuation of U.S. patent application Ser. No.12/866,558, now U.S. Pat. No. 8,673,555, which is a Section 371 U.S.national stage entry of International Patent Application NoPCT/US2009/033793, filed Feb. 11, 2009, which claims priority to expiredU.S. Provisional Patent Application No. 61/029,221, filed Feb. 15, 2008,the contents of which are hereby incorporated by reference in theirentireties.

BACKGROUND

1. Technical Field

This document relates to methods and materials involved in detectingpremalignant and malignant neoplasms (e.g., colorectal and pancreaticcancer).

2. Background Information

About half of all cancer deaths in the United States result fromaero-digestive cancer. For example, of the estimated annual cancerdeaths, about 25 percent result from lung cancer, about 10 percentresult from colorectal cancer; about 6 percent result from pancreascancer, about 3 percent result from stomach cancer; and about 3 percentresult from esophagus cancer. In addition, over 7 percent of the annualcancer deaths result from other aero-digestive cancers such asnaso-oro-pharyngeal, bile duct, gall bladder, and small bowel cancers.

SUMMARY

This document relates to methods and materials for detectingpremalignant and malignant neoplasms (e.g., colorectal and pancreaticcancer). For example, this document provides methods and materials thatcan be used to determine whether a sample (e.g., a stool sample) from amammal contains a marker for a premalignant and malignant neoplasm suchas a marker from a colonic or supracolonic aero-digestive neoplasmlocated in the mammal. The detection of such a marker in a sample from amammal can allow a clinician to diagnose cancer at an early stage. Inaddition, the analysis of a sample such as a stool sample can be muchless invasive than other types of diagnostic techniques such asendoscopy.

This document is based, in part, on the discovery of particular nucleicacid markers, polypeptide markers, and combinations of markers presentin a biological sample (e.g., a stool sample) that can be used to detecta neoplasm located, for example, in a mammal's small intestine, gallbladder, bile duct, pancreas, liver, stomach, esophagus, lung, ornaso-oro-pharyngeal airways. For example, as described herein, stool canbe analyzed to identify mammals having cancer. Once a particular mammalis determined to have stool containing a neoplasm-specific marker orcollection of markers, additional cancer screening techniques can beused to identify the location and nature of the neoplasm. For example, astool sample can be analyzed to determine that the patient has aneoplasm, while magnetic resonance imaging (MRI), endoscopic analysis,and tissue biopsy techniques can be used to identify the location andnature of the neoplasm. In some cases, a combination of markers can beused to identify the location and nature of the neoplasm withoutadditional cancer screening techniques such as MRI, endoscopic analysis,and tissue biopsy techniques.

In general, one aspect of this document features a method of detectingpancreatic cancer in a mammal. The method comprises, or consistsessentially of determining the ratio of an elastase 3A polypeptide to apancreatic alpha-amylase polypeptide present within a stool sample. Thepresence of a ratio greater than about 0.5 indicates that the mammal haspancreatic cancer. The presence of a ratio less than about 0.5 indicatesthat the mammal does not have pancreatic cancer.

In another aspect, this document features a method of detectingpancreatic cancer in a mammal. The method comprises or consistsessentially of determining the level of an elastase 3A polypeptide in astool sample from the mammal. The presence of an increased level of anelastase 3A polypeptide, when compared to a normal control level, isindicative of pancreatic cancer in the mammal.

In another aspect, this document features a method of detectingpancreatic cancer in a mammal. The method comprises, or consistsessentially of, determining the level of a carboxypeptidase Bpolypeptide in a stool sample from the mammal. An increase in the levelof a carboxypeptidase B polypeptide, when compared to a normal controllevel, is indicative of pancreatic cancer in the mammal.

In another aspect, this document features a method of detectingpancreatic cancer in a mammal. The method comprises, or consistsessentially of, determining whether or not a stool sample from themammal comprises a ratio of a carboxypeptidase B polypeptide to acarboxypeptidase A2 polypeptide that is greater than about 0.5. Thepresence of the ratio greater than about 0.5 indicates that the mammalhas pancreatic cancer.

In another aspect, this document features a method of detecting canceror pre-cancer in a mammal. The method comprises, or consists essentiallyof, determining whether or not a stool sample from the mammal has anincrease in the number of DNA fragments less than 200 base pairs inlength, as compared to a normal control. The presence of the increase inthe number of DNA fragments less than 200 base pairs in length indicatesthat the mammal has cancer or pre-cancer. The DNA fragments can be lessthan 70 base pairs in length.

In another aspect, this document features a method of detectingcolorectal cancer or pre-cancer in a mammal. The method comprises, orconsists essentially of, determining whether or not a stool sample fromthe mammal has an elevated K-ras (Kirsten rat sarcoma-2 viral(v-Ki-ras2) oncogene homolog (GenBank accession no. NM_033360;gi|34485724|)) mutation score, an elevated BMP3 (bone morphogeneticprotein 3 (GenBank accession no. M22491; gi|179505)) methylation status,and an elevated level of human DNA as compared to a normal control. Thepresence of the elevated K-ras mutation score, elevated BMP3 methylationstatus, and elevated level of human DNA level indicates that the mammalhas colorectal cancer or pre-cancer. The K-ras mutation score can bemeasured by digital melt curve analysis. The K-ras mutation score can bemeasured by quantitative allele specific PCR.

In another aspect, this document features a method of detectingaero-digestive cancer or pre-cancer in a manual. The method comprises,or consists essentially of, determining whether or not a stool samplefrom the mammal has an elevated K-ras mutation score, an elevated BMP3methylation status, and an elevated level of human DNA as compared to anormal control. The presence of the elevated K-ras mutation score,elevated BMP3 methylation status, and elevated level of human DNA levelindicates that the mammal has aero-digestive cancer or pre-cancer. TheK-ras mutation score can be measured by digital melt curve analysis. TheK-ras mutation score can be measured by quantitative allele-specificPCR. The method can further comprise determining whether or not a stoolsample from the mammal has an elevated APC mutation score. The APCmutation score can be measured by digital melt curve analysis.

In another aspect, this document features a method of detectingaero-digestive cancer or pre-cancer in a mammal. The method comprises,or consists essentially of determining whether or not the mammal has atleast one mutation in six nucleic acids selected from the groupconsisting of p16, p53, k-ras. APC (adenomatosis polyposis coli tumorsuppressor (GenBank accession no. NM_000038; gi|189011564)), SMAD4 (SMADfamily member 4 (GenBank accession no. NM_005359; gi|195963400)). EGFR(epidermal growth factor receptor (GenBank accession no. NM_005228;gi|41327737)). CTNNB1 (catenin (cadherin-associated protein), beta 1 (88kD) (GenBank accession no. X87838; gi|11548531)), and BRAF (B-Rafproto-oncogene serinethreonine-protein kinase (p94) (GenBank accessionno. NM_004333; gi|1876086321)) nucleic acids. The presence of at leastone mutation in each of the six nucleic acids indicates that the mammalhas aero-digestive cancer or pre-cancer. The method can further comprisedetermining whether or not a stool sample from the mammal has anelevated level of a carboxvpeptidase B polypeptide as compared to anormal control. The presence of the elevated level of a caboxypeptidaseB polypeptide indicates that the mammal has aero-digestive cancer orpre-cancer in the mammal. The method can further comprise determiningwhether or not a stool sample from the mammal has an elevated amount ofDNA fragments less than 70 base pairs in length as compared to a normalcontrol. The presence of the elevated amount of DNA fragments less than70 base pairs in length indicates that the mammal has aero-digestivecancer or pre-cancer. The method can further comprise determiningwhether or not a stool sample from the mammal has an elevated amount ofDNA fragments greater than 100 base pairs in length as compared tonormal controls. The presence of the elevated amount of DNA fragmentsgreater than 100) base pairs in length indicates that the mammal hasaero-digestive cancer or pre-cancer. The method can further comprisedetermining whether or not a stool sample from the mammal has anelevated BMP3 methylation status. The elevated BMP3 methylation statuslevel indicates that the mammal has aero-digestive cancer or pre-cancer.The determining step can comprise using digital melt curve analysis.

In another aspect, this document features a method of detectingaero-digestive cancer or pre-cancer in a mammal. The method comprises,or consists essentially of, measuring mutations in a matrix marker panelin a stool sample. The marker panel can comprise measuring DNA mutationsin p16, p53, k-ras. APC, SMAD4. EGFR. CTNNB1, and BRAF nucleic acids.The presence of a mutation in each of the nucleic acids is indicative ofthe presence of aero-digestive cancer or pre-cancer in a mammal.

In another aspect, this document features a method of detectingaero-digestive cancer in a mammal. The method comprises, or consistsessentially of, determining whether or not the methylation status of anALX4 (aristaless-like homeobox 4 (GenBank accession no. AF294629;gi|10863748|)) nucleic acid in a stool sample from the mammal iselevated, as compared to a normal control. The presence of an elevatedALX4 methylation status indicates the presence of aero-digestive cancerin the mammal.

In another aspect, this document features a method of diagnosingpancreatic cancer in a mammal. The method comprises, or consistsessentially of, obtaining a stool sample from the mammal, determiningthe ratio of an elastase 3A polypeptide to a pancreatic alpha-amylasepolypeptide present within a stool sample, and communicating a diagnosisof pancreatic cancer if the ratio is greater than about 0.5, therebydiagnosing the mammal with pancreatic cancer.

In another aspect, this document features a method of diagnosing amammal with pancreatic cancer. The method comprises, or consistsessentially of, obtaining a stool sample from the mammal, measuringmutations in a matrix marker panel of nucleic acids present in thesample, determining the ratio of a carboxypeptidase B polypeptide to acarboxypeptidase A2 polypeptide present within the sample, andcommunicating a diagnosis of pancreatic cancer or pre-cancer if amutation is detected in each of the marker panel nucleic acids and theratio is greater than 0.5, thereby diagnosing the mammal. The matrixmarker panel comprises or consists essentially of p16, p53, k-ras. APC,SMAD4. EGFR. CTNNB1, and BRAF nucleic acids.

In another aspect, this document features method of diagnosing a mammalwith colorectal cancer. The method comprises, or consists essentiallyof, obtaining a stool sample from the mammal, detecting mutations in amatrix marker panel comprising of p16, p53, k-ras, APC. SMAD4. EGFR.CTNNB1, and BRAF nucleic acids in DNA present within the sample,measuring the level of a serotransferrin polypeptide present within thesample, and communicating a diagnosis of colorectal cancer or pre-cancerif a mutation is detected in each of the nucleic acids and the level ofa serotransferrin polypeptide is elevated as compared to a referencelevel, thereby diagnosing the mammal.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention pertains. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of the present invention, suitable methods andmaterials are described below. All publications, patent applications,patents, and other references mentioned herein are incorporated byreference in their entirety. In case of conflict, the presentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

The details of one or more embodiments of the invention are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the invention will be apparent from thedescription and from the claims.

DESCRIPTION OF THE DRAWINGS

FIG. 1: Adjusted Cut-off Levels with Quantitative Stool Markers toAchieve 95% Specificity across Age and Gender using the Q-LEAD Model.Solid line for women, dotted line for men.

FIG. 2: Sensitive and specific detection of pancreatic cancer by fecalratio of carboxypeptidase B:carboxypeptidase A2. Note that ratio instools from patients with colorectal cancer is no different from ratioswith healthy controls.

FIG. 3: Elastase levels quantified in stools from patients withpancreatic cancer, patients with colorectal cancer, and healthy controls

FIG. 4: Ratio of elastase 3A:pancreatic alpha amylase differentiatespatients with pancreatic cancer from patients with colorectal cancer andfrom healthy controls.

FIG. 5: Digital Melt Curve to detect mutations by targeted gene scanning(temperature (x-axis) v. temperature-normalized fluorescence (y-axis)).Eight pairs of primers, which amplify 100-350 bp gene fragments, wereused to scan K-ras and APC genes and detect mutations (substitution anddeletion mutations, respectively) at 1% mutant/wild type ratio.

FIG. 6: Quantitive detection of low abundance mutations by Digital MeltCurve Assay. Varying Mutant: Wild-type ratios of K-ras and APC genemixtures were prepared and assayed blindly by Digital Melt Curve.

FIG. 7: High analytical sensitivity by Digital Melt Curve (temperature(x-axis) v. temperature-normalized fluorescence (y-axis)). To test thedetection limit of digital melt curve (DMC) assay, mutant copies werespiked in wild-type copies at 0.1, 0.5, 1, 5, and 10% dilutions. DMCcould detect up to 0.1% mutant/wild-type level when 1000 copies weredispersed to one 96-well plate. The numbers of positive wells increasedproportionally when spiked mutant copies were increased. A pair ofprimers that amplify 248 bp K-ras gene fragment were used as an examplehere. Primers that amplify 119 bp K-ras gene, 162 bp APC gene, and 346bp APC genes were also used to test the detection limit and quantitativeproperty of DMC.

FIG. 8: Superior screen detection of colorectal precancerous polyps byDigital Melt Curve (DMC). Histogram compares sensitivity by DMC withthat by common fecal occult blood tests (Hemoccult and HemoccultSENSA)and by the commercial stool DNA test (PreGenPlus, Exact Sciences).Detection by DMC was significantly better than by any other test(p<0.05).

FIG. 9: Distributions of short fragment human DNA (short DNA) and longfragment human DNA (long DNA) in stools from patients with normalcolonoscopy, large precancerous adenomas, and colorectal cancer. HumanDNA quantified by an assay of Alu repeats. Short DNA represents 45 bpfragment amplification products, and long DNA represents 245 bpamplification products.

FIG. 10: Stool distributions of short and long DNA in patients withpancreatic cancer and in healthy controls. Short DNA represents 45 bpfragment amplification products, and long DNA represents 245 bpamplification products.

FIG. 11: Methylated BMP3 gene in stool for detection of colorectalneoplasia. Methylated BMP3 was blindly quantified in stools frompatients with colorectal cancers, precancerous adenomas, and normalindividuals with real-time methylation-specific PCR. Each circlerepresents a stool sample.

FIG. 12: Frequency of Specific Base Changes in Colorectal Tumors.

DETAILED DESCRIPTION

This document provides methods and materials related to detecting aneoplasm in a mammal (e.g., a human). For example, this documentprovides methods and materials for using nucleic acid markers,polypeptide markers, and combinations of markers present in a biologicalsample (e.g., a stool sample) to detect a neoplasm in a mammal. Such aneoplasm can be a cancer or precancer in the head and neck, lungs andairways, esophagus, stomach, pancreas, bile ducts, small bowel, orcolorectum. It will be appreciated that the methods and materialsprovided herein can be used to detect neoplasm markers in a mammalhaving a combination of different neoplasms. For example, the methodsand materials provided herein can be used to detect nucleic acid andpolypeptide markers in a human having lung and stomach neoplasms.

In some cases, the methods and materials provided herein can be used toquantify multiple markers in biological samples (e.g., stool) to yieldhigh sensitivity for detection of lesions (e.g., neoplasms), whilepreserving high specificity. Such methods can include, for example, alogistic model that adjusts specificity cut-offs based on age, gender,or other variables in a target population to be tested or screened.

In some cases, the methods and materials provided herein can be used todetermine whether a mammal (e.g., a human) has colorectal cancer orpancreatic cancer. For example, serotransferin, methylated BMP3, andmutant BRAF markers in stool can be used to identify a mammal as likelyhaving colorectal cancer, while mutant p16, carboxypeptidase B/A, andelastase 2A markers can be used to identify a mammal as likely havingpancreatic cancer.

Any suitable method can be used to detect a nucleic acid marker in amammalian stool sample. For example, such methods can involve isolatingDNA from a stool sample, separating out one or more particular DNAs fromthe total DNA, subjecting the DNAs to bisulfite treatment, anddetermining whether the separated DNAs are abnormally methylated (e.g.,hypermethylated or hypomethylated). In some cases, such methods caninvolve isolating DNA from a stool sample and determining the presenceor absence of DNA having a particular size (e.g., short DNA). It isnoted that a single stool sample can be analyzed for one nucleic acidmarker or for multiple nucleic acid markers. For example, a stool samplecan be analyzed using assays that detect a panel of different nucleicacid markers. In addition, multiple stool samples can be collected froma single mammal and analyzed as described herein.

Nucleic acid can be isolated from a stool sample using, for example, akit such as the QIAamp DNA Stool Mini Kit (Qiagen Inc., Valencia,Calif.). In addition, nucleic acid can be isolated from a stool sampleusing the following procedure: (1) homogenizing samples in an excessvolume (>1-7 w:v) of a stool stability buffer (0.5M Tris pH 9.0, 150 mMEDTA, 10 mM NaCl) by shaking or mechanical mixing; (2) centrifuging a 10gram stool equivalent of each sample to remove all particulate matter;(3) adding 1 μL of 100 μg/μL RNase A to the supernatant and incubatingat 37° C. for 1 hour, (4) precipitating total nucleic acid with 1/10volume 3M NaAc and an equal volume isopropanol; and (5) centrifuging andthen resuspending the DNA pellet in TE (0.01 M Tris pH 7.4, 0.001 MEDTA). U.S. Pat. Nos. 5,670,325; 5,741,650; 5,928,870, 5,952,178, and6,020,137 also describe various methods that can be used to prepare andanalyze stool samples.

One or more specific nucleic acid fragments can be purified from anucleic acid preparation using, for example, a modifiedsequence-specific hybrid capture technique (see, e.g., Ahlquist et al.(2000) Gastroenterology, 119:1219-1227). Such a protocol can involve:(1) adding 300 μL of sample preparation to an equal volume of a 6 Mguanidine isothiocyanate solution containing 20 pmol biotinylatedoligonucleotides (obtained from, for example, Midland Certified ReagentCo, Midland. Tex.) with sequences specific for the DNA fragments to beanalyzed; (2) incubating for two hours at 25° C.; (3) addingstreptavidin coated magnetic beads to the solution and incubating for anadditional hour at room temperature; (4) washing the bead/hybrid capturecomplexes four times with IX B+W buffer (1 M NaCl, 0.01 M Tris-HCl pH7.2, 0.001 M EDTA, 0.1% Tween 20); and (5) eluting the sequence specificcaptured DNA into 35 μL L-TE (1 mM Tris pH 7.4, 0.1 M EDTA) by heatdenaturation of the bead/hybrid capture complexes. Any other suitabletechnique also can be used to isolate specific nucleic acid fragments.

Nucleic acid can be subjected to bisulfite treatment to convertunmethylated cytosine residues to uracil residues, while leaving any5-methylctosine residues unchanged. A bisulfite reaction can beperformed using, for example, standard techniques: (1) denaturingapproximately 1 μg of genomic DNA (the amount of DNA can be less whenusing micro-dissected DNA specimens) for 15 minutes at 45° C. with 2 NNaOH. (2) incubating with 0.1 M hydroquinone and 3.6 M sodium bisulfite(pH 5.0) at 55° C. for 4-12 hours; (3) purifying the DNA from thereaction mixture using standard (e.g., commercially-available) DNAminiprep columns or other standard techniques for DNA purification; (4)resuspending the purified DNA sample in 55 μL water and adding 5 μl 3 NNaOH for a desulfonation reaction that typically is performed at 40° C.for 5-10 minutes; (5) precipitating the DNA sample with ethanol, washingthe DNA, and resuspending the DNA in an appropriate volume of water.Bisulfite conversion of cytosine residues to uracil also can be achievedusing other methods (e.g., the CpGenome™ DNA Modification Kit fromSerologicals Corp., Norcross, Ga.).

Any appropriate method can be used to determine whether a particular DNAis hypermethylated or hypomethylated. Standard PCR techniques, forexample, can be used to determine which residues are methylated, sinceunmethylated cytosines converted to uracil are replaced by thymidineresidues during PCR. PCR reactions can contain, for example, 10 μL ofcaptured DNA that either has or has not been treated with sodiumbisulfite, IX PCR buffer, 0.2 mM dNTPs, 0.5 μM sequence specific primers(e.g., primers flanking a CpG island within the captured DNA), and 5units DNA polymerase (e.g., Amplitaq DNA polymerase from PE AppliedBiosystems. Norwalk, Conn.) in a total volume of 50 μl. A typical PCRprotocol can include, for example, an initial denaturation step at 94°C. for 5 min, 40 amplification cycles consisting of 1 minute at 94° C.,1 minute at 60° C., and 1 minute at 72° C., and a final extension stepat 72° C. for 5 minutes.

To analyze which residues within a captured DNA are methylated, thesequences of PCR products corresponding to samples treated with andwithout sodium bisulfite can be compared. The sequence from theuntreated DNA will reveal the positions of all cytosine residues withinthe PCR product Cytosines that were methylated will be converted tothymidine residues in the sequence of the bisulfite-treated DNA, whileresidues that were not methylated will be unaffected by bisulfitetreatment.

Purified nucleic acid fragments from a stool sample or samples can beanalyzed to determine the presence or absence of one or more somaticmutations. Mutations can be single base changes, shortinsertion/deletions, or combinations thereof. Methods of analysis caninclude conventional Sanger based sequencing, pyrosequencing, nextgeneration sequencing, single molecule sequencing, and sequencing bysynthesis. In some cases, mutational status can be determined by digitalPCR followed by high resolution melting curve analysis. In other cases,allele specific primers or probes in conjunction with amplificationmethods can be used to detect specific mutations in stool DNA. Themutational signature can comprise not only the event of a base orsequence change in a specific gene, but also the location of the changewithin the gene, whether it is coding, non-coding, synonymous ornon-synonymous, a transversion or transition, and the dinucleotidesequence upstream and downstream from the alteration.

In some cases, a sample can be assessed for the presence or absence of apolypeptide marker. For example, any appropriate method can be used toassess a stool sample for a polypeptide marker indicative of a neoplasm.For example, a stool sample can be used in assays designed to detect oneor more polypeptide markers. Appropriate methods such as those describedelsewhere (Aebersold and Mann, Nature, 422:198-207 (2003) and McDonaldand Yates. Dis. Markers. 18:99-105 (2002)) can be adapted or designed todetect polypeptides in a stool. For example, single-reaction monitoringusing a TSQ mass spectrometer can specifically target polypeptides in astool sample. High resolution instruments like the LTQ-FT or LTQorbitrap can be used to detect polypeptides present in a stool sample.

The term “increased level” as used herein with respect to the level ofan elastase 3A polypeptide is any level that is above a median elastase3A polypeptide level in a stool sample from a random population ofmammals (e.g., a random population of 10, 20, 30, 40, 50, 100, or 500mammals) that do not have an aero-digestive cancer. Elevated polypeptidelevels of an elastase 3A polypeptide can be any level provided that thelevel is greater than a corresponding reference level. For example, anelevated level of an elastase 3A polypeptide can be 0.5, 1, 2, 3, 4, 5,6, 7, 8, 9, 10, or more fold greater than the reference level ofelastase 3A polypeptide in a normal sample. It is noted that a referencelevel can be any amount. For example, a reference level for an elastase3A polypeptide can be zero. In some cases, an increased level of anelastase 3A polypeptide can be any detectable level of an elastase 3Apolypeptide in a stool sample.

The term “increased level” as used herein with respect to the level ofan carboxypeptidase B polypeptide level is any level that is above amedian carboxypeptidase B polypeptide level in a stool sample from arandom population of mammals (e.g., a random population of 10, 20, 30,40, 50, 100, or 500 mammals) that do not have an aero-digestive cancer.Elevated polypeptide levels of carboxypeptidase B polypeptide can be anylevel provided that the level is greater than a corresponding referencelevel. For example, an elevated level of carboxypeptidase B polypeptidecan be 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more fold greater than thereference level carboxypeptidase B polypeptide observed in a normalstool sample. It is noted that a reference level can be any amount. Forexample, a reference level for a carboxypeptidase B polypeptide can bezero. In some cases, an increased level of a carboxypeptidase Bpolypeptide can be any detectable level of a carboxypeptidase Bpolypeptide in a stool sample.

The term “increased level” as used herein with respect to the level ofDNA fragments less than about 200 or less than about 70 base pairs inlength is any level that is above a median level of DNA fragments lessthan about 200 or less than about 70 base pairs in length in a stoolsample from a random population of mammals (e.g., a random population of10, 20, 30, 40, 50, 100, or 500 mammals) that do not have anaero-digestive cancer. In some cases, an increased level of DNAfragments less than about 200 or less than about 70 base pairs in lengthcan be any detectable level of DNA fragments less than about 200 or lessthan about 70 base pairs in length in a stool sample.

The term “elevated methylation” as used herein with respect to themethylation status of a BMP3 or ALX nucleic acid is any methylationlevel that is above a median methylation level in a stool sample from arandom population of mammals (e.g., a random population of 10, 20, 30,40, 50, 100, or 500 mammals) that do not have an aero-digestive cancer.Elevated levels of BMP3 or ALX methylation can be any level providedthat the level is greater than a corresponding reference level. Forexample, an elevated level of BMP3 or ALX methylation can be 0.5, 1, 2,3, 4, 5, 6, 7, 8, 9, 10, or more fold greater than the reference levelmethylation observed in a normal stool sample. It is noted that areference level can be any amount.

The term “elevated mutation score” as used herein with respect todetected mutations in a matrix panel of particular nucleic acid markersis any mutation score that is above a median mutation score in a stoolsample from a random population of mammals (e.g., a random population of10, 20, 30, 40, 50, 100, or 500 mammals) that do not have anaero-digestive cancer. An elevated mutation score in a matrix panel ofparticular nucleic acid markers can be any score provided that the scoreis greater than a corresponding reference score. For example, anelevated score of K-ras or APC mutations can be 0.5, 1, 2, 3, 4, 5, 6,7, 8, 9, 10, or more fold greater than the reference score of K-ras orAPC mutations observed in a normal stool sample. It is noted that areference score can be any amount.

In some cases, a ratio of particular polypeptide markers can bedetermined and used to identify a mammal having an aero-digestive cancer(e.g., a colorectal cancer or a pancreatic cancer). For example, a ratioprovided herein (e.g., the ratio of carboxypeptidase B polypeptidelevels to carboxypeptidase A2 polypeptide levels) to can be used asdescribed herein to identify a mammal having a particular neoplasm(e.g., pancreatic cancer).

In some cases, a matrix marker panel can be used to identify mammalshaving an aero-digestive cancer (e.g., a colorectal cancer or apancreatic cancer). In some cases, such panel also can identify thelocation of the aero-digestive cancer. Such a panel can include nucleicacid markers, polypeptide markers, and combinations thereof and canprovide information about a mutated marker gene, the mutated region ofthe marker gene, and/or type of mutation. For example, data can beanalyzed using a statistical model to predict tumor site (e.g.,anatomical location or tissue of origin) based on inputs from sequencingdata (such as by specific nucleic acid or combination of nucleic acidsmutated, specific mutational location on a nucleic acid, and nature ofmutation (e.g. insertion, deletion, transition, or transversion) or byany combination thereof) and/or data from polypeptide or other types ofmarkers. For example, a Site of Tumor Estimate (SITE) model can be usedto predict tumor site using a matrix panel of markers that are presentto variable extent across tumors.

In some cases, data can be analyzed using quantified markers to create alogistic model, which can have both high sensitivity and highspecificity. For example, a logistic model can also incorporatepopulation variables like gender and age to adjust cut-off levels fortest positivity and thereby optimize assay performance in a screeningsetting. In some cases, a Quantitative Logistic to Enhance AccurateDetection (Q-LEAD) Model can be used with any marker class orcombination of markers as long as they can be quantified.

This document also provides methods and materials to assist medical orresearch professionals in determining whether or not a mammal has anaero-digestive cancer. Medical professionals can be, for example,doctors, nurses, medical laboratory technologists, and pharmacists.Research professionals can be, for example, principle investigators,research technicians, postdoctoral trainees, and graduate students. Aprofessional can be assisted by (1) determining the ratio of particularpolypeptide markers in a stool sample, and (2) communicating informationabout the ratio to that professional, for example. In some cases, aprofessional can be assisted by (1) determining the level of human DNA,the methylation status of genes such as BMP3, and the mutation score ofgenes such as APC and K-ras, and (2) communicating information about thelevel of DNA, the methylation status of particular genes, and themutation score of particular genes to the professional. In some cases, aprofessional can be assisted by (1) detecting mutations incancer-related genes such as K-ras, p53, APC, p16. EGFR. CTNNB1. BRAF,and SMAD4, as a matrix marker panel, and (2) communicating informationregarding the mutations to the professional.

After the ratio of particular polypeptide markers, or presence ofparticular nucleic acid markers in a stool sample is reported, a medicalprofessional can take one or more actions that can affect patient care.For example, a medical professional can record the results in apatient's medical record. In some cases, a medical professional canrecord a diagnosis of an aero-digestive cancer, or otherwise transformthe patient's medical record, to reflect the patient's medicalcondition. In some cases, a medical professional can review and evaluatea patient's entire medical record, and assess multiple treatmentstrategies, for clinical intervention of a patient's condition. In somecases, a medical professional can record a tumor site prediction withthe reported mutations. In some cases, a medical professional canrequest a determination of the ratio of particular polypeptide markersto predict tumor site. In some cases, a medical professional can reviewand evaluate a patient's entire medical record and assess multipletreatment strategies, for clinical intervention of a patient'scondition.

A medical professional can initiate or modify treatment of anaero-digestive cancer after receiving information regarding a ratio ofparticular polypeptide markers or the presence of nucleic acid markersin a patient's stool sample. In some cases, a medical professional cancompare previous reports and the recently communicated ratio ofparticular polypeptide markers, or presence of nucleic acid markers, andrecommend a change in therapy. In some cases, a medical professional canenroll a patient in a clinical trial for novel therapeutic interventionof an aero-digestive cancer. In some cases, a medical professional canelect waiting to begin therapy until the patient's symptoms requireclinical intervention.

A medical professional can communicate the ratio of particular polypeptide markers to a patient or a patient's family. In some cases, amedical professional can provide a patient and/or a patient's familywith information regarding aero-digestive cancers, including treatmentoptions, prognosis, and referrals to specialists, e.g., oncologistsand/or radiologists. In some cases, a medical professional can provide acopy of a patient's medical records to communicate the ratio ofparticular polypeptide markers to a specialist.

A research professional can apply information regarding a subject'sratio of particular polypeptide markers to advance aero-digestive cancerresearch. For example, a researcher can compile data on the ratio ofparticular polypeptide markers, and/or presence of particular nucleicacid markers, with information regarding the efficacy of a drug fortreatment of aero-digestive cancer to identify an effective treatment.In some cases, a research professional can obtain a subject's ratio ofparticular polypeptide markers, and/or determine the presence ofparticular nucleic acid markers to evaluate a subject's enrollment, orcontinued participation in a research study or clinical trial. In somecases, a research professional can classify the severity of a subject'scondition, based on the ratio of particular polypeptide markers and/orthe levels of particular nucleic acid markers. In some cases, a researchprofessional can communicate a subject's ratio of particular polypeptidemarkers, and/or the presence of particular nucleic acid markers to amedical professional. In some cases, a research professional can refer asubject to a medical professional for clinical assessment of anaero-digestive cancer, and treatment of an aero-digestive cancer.

Any appropriate method can be used to communicate information to anotherperson (e.g., a professional). For example, information can be givendirectly or indirectly to a professional. For example, a laboratorytechnician can input the ratio of particular polypeptide markers and/orparticular nucleic acid markers into a computer-based record. In somecases, information is communicated by making a physical alteration tomedical or research records. For example, a medical professional canmake a permanent notation or flag a medical record for communicating adiagnosis to other medical professionals reviewing the record. Inaddition, any type of communication can be used to communicate theinformation. For example, mail, e-mail, telephone, and face-to-faceinteractions can be used. The information also can be communicated to aprofessional by making that information electronically available to theprofessional. For example, the information can be communicated to aprofessional by placing the information on a computer database such thatthe professional can access the information. In addition, theinformation can be communicated to a hospital, clinic, or researchfacility serving as an agent for the professional.

The invention will be further described in the following examples, whichdo not limit the scope of the invention described in the claims.

EXAMPLES Example 1 Multi-Marker Quantitation and a Q-LEAD Model

Most approaches at marker detection in stool have been qualitative. Whensuch qualitative approaches are applied to assay of multiple markers(targeting multiple markers is required with neoplasm detection duemolecular heterogeneity), sensitivity is achieved at the expense ofcompounded non-specificity Non-specificity can lead to prohibitiveprogrammatic cost with population screening due to the expensive andunnecessary evaluations of false-positive tests. However, if markers arequantified, then a logistic model can be created to achieve both highsensitivity and high specificity. Such a logistic model can alsoincorporate population variables like gender and age to adjust cut-offlevels for test positivity and thereby optimize assay performance in ascreening setting (FIG. 1). This Quantitative Logistic to EnhanceAccurate Detection (Q-LEAD) Model can be used with any marker class orcombination of markers as long as they can be quantified.

A combination of more than one marker was undertaken to achieve thedesired sensitivity and specificity for cancer detection. Binaryregression methods predicting disease as a function of diagnostic testsestimate the optimal combination of the tests for classifying a subjectas diseased or not. McIntosh and Pepe, Biometrics 58: 657-664 (2002). Alogistic regression model can assess the relationship between a binarydependent response variable such as presence or absence of disease andone or more independent predictor variables. The independent predictorsmay be qualitative (e.g., binary) or quantitative (e.g., a continuousendpoint). In the Q-LEAD model, the independent predictors can includesuch biological markers as K-ras, BMP3 and DNA concentration, andothers. Importantly, the model incorporates the demographic variables ofgender and age, as we have observed that both age and gender influencemolecular marker levels in stool. As average stool marker levelsincrease with age and male gender, failure to adjust for these variableswould yield suboptimal specificity in men and elderly persons tested.Coefficients are estimated from the sample data for each term in themodel. The result of the model is a risk score for each subject. Cutoffsfor predicting disease state from this risk score can be determined inorder to maximize sensitivity and specificity of the marker combinationsfor predicting disease as desired. The inclusion of demographicvariables allows these cutoffs to be determined as a function of age andgender.

As an application of the Q-LEAD Model, the following was performed toevaluate a quantitative stool DNA assay approach targeting threeinformative markers for the detection of colorectal neoplasia. Subjectsincluded 34 with colorectal cancer, 20 with adenomas >1 cm, and 26 withnormal colonoscopy. Subjects added a DNA stabilization buffer with stoolcollection, and stools were frozen at −80° C. within 72 hours. Fromthawed stool aliquots, crude DNA was extracted by standard methods, andtarget genes were enriched by sequence capture. K-ras mutation score,methylation of BMP3 gene, and concentration of human DNA (245 bp length)were respectively quantified by a digital melt curve assay, real-timemethylation-specific PCR, and real-time Alu PCR, respectively. Assayswere performed blinded. A logistic model, which incorporates threemarkers and gender, was constructed to analyze discrimination bycombined markers.

Age medians were 60 for patients with colorectal cancer, 66 for thosewith adenomas, and 61 for normal controls; and male/female distributionswere 23/11, 9/11, and 10/16, respectively. Detection rates of colorectalneoplasms were determined by individual quantitative markers atspecificity cut-offs of 96 percent and by combined markers (Table 1).Discrimination by combined markers was calculated using a qualitativebinomial method (each marker considered as positive or negative based onindividual 96 percent specificity) and by the Q-LEAD model (sensitivitydata shown at overall specificity of 96 percent).

TABLE 1 Specificity and sensitivity of cancer markers. SensitivitySpecificity Cancers Adenomas Both Individual Markers k-ras mutation 96%42% 32% 38% BMP3 methylation 96% 45% 32% 40% DNA concentration 96% 65%40% 56% Combined Markers Qualitative method 88% 90% 58% 78% Q-LEAD Model96% 90% 47% 76%

By quantitative assay and multivariable analysis of an informativemarker panel, stool DNA testing can achieve high sensitivity whilepreserving high specificity for detection of colorectal neoplasia. Theparticular three-marker combination of mutant K-ras. BMP3 methylation,and human DNA concentration represents a complementary, high-yieldpanel.

The above data set and additional data were analyzed as follows. Aquantitative stool DNA assay approach targeting four informative markersfor use in the detection of colorectal cancer and advanced adenoma wasevaluated. Subjects comprised 74 patients with colorectal cancer, 27with an adenoma >1 cm, and 100 with normal colonoscopy. Stools werecollected with a stabilization buffer before or >1 week aftercolonoscopy and were frozen at −80° C. within 24 hours of collection.From thawed stool aliquots, crude DNA was extracted as described above,and target genes were enriched by sequence-specific capture. Human DNAconcentration, K-ras and APC mutation scores, and BMP3 methylation weresensitively quantified by real-time Alu PCR, a digital melt curve assay(Zou et al., Gastroenterology, “High Detection Rates of ColorectalNeoplasia by Stool DNA Testing With a Novel Digital Melt Curve Assay.”(2008)), and real-time methylation-specific PCR, respectively. Assayswere performed blindly. Sensitivities and specificities of single makersand their combinations were analyzed.

Age medians were 61 for patients with colorectal cancer, 67 for thosewith adenomas, and 59 for normal controls; and, male/female ratios were52/22, 15/12, and 37/63, respectively. The table displays detectionrates of colorectal neoplasms by individual quantitative markers atspecificities of 90% and by combined markers at two specificities (Table2) Data in this table represent a training set and have not beenadjusted for age and gender. Yet, it is clear that the full panel ofAlu, K-ras, APC, and BMP3 detected more neoplasms than any individualmarker, p<0.05 At 90% specificity, the full panel detects moreadenomas >3 cm (90%, 9/10) than <3 cm (47%, 8/17), p<0.05, and morecolorectal cancers at stages III-IV (89%, 40/45) than at stages I-II(69%, 20/29) p<0.05. Neoplasm detection rates were not affected by tumorlocation.

TABLE 2 Specificity and sensitivity of a four marker panel SensitivityColorectal cancers Adenomas Specificity I-II III-IV ≦3 cm >3 cmIndividual Markers APC mutation 90% 38% 40% 47% 50% k-ras mutation 90%46% 42% 24% 50% BMP3 methylation 90% 36% 38% 12% 30% DNA concentration90% 52% 76% 41% 50% Combined Markers 90% 69% 89% 47% 90%

In conclusion, a quantitative stool DNA assay system that incorporates astabilization buffer with specimen collection, high analyticalsensitivity, and a panel of broadly informative markers can achieve highdetection rates of both colorectal cancers and advanced adenoma.

Example 2 SITE Model and Matrix Marker Panel

A statistical model (Site of Tumor Estimate (SITE)) can be used topredict tumor site (e.g., anatomical location or tissue of origin) basedon inputs from sequencing data (such as by specific nucleic acid orcombination of nucleic acids mutated, specific mutational location on anucleic acid, and nature of mutation (e.g. insertion, deletion,transition, or transversion) or by any combination thereof) and/or datafrom polypeptide or other types of markers.

A matrix marker panel was developed to include eight cancer-relatedgenes: K-ras, p53. APC, p16, EGFR, CTNNB1, BRAF, and SMAD4. The mutationfrequencies of these genes were tabulated against the six majoraero-digestive cancers based on literature or public database reviewsand on actual sequencing observations (Table 3). Literature frequencieswere derived from the COSMIC somatic mutation database, review articles,and texts.

TABLE 3 Matrix Panel of Markers by Tumor Site AD Cancer Site N p16 p53K-ras APC SMAD4 EGFR CTNNB1 BRAF Total Unique Literature Colorectal <5%50-75% 40% 85% 14% NA 13%  20% Pancreatic 85-100%     50-60% 80-90%   10-40%    30% NA 3-8%  <5% Lung 15-25%    25-75% 20-40%     5%  7% 30%6% <5% Bile Duct 15-60%    30-60% 40% 30-40%    17% NA 1% 14% Gastric5-30%  20-50% 10% 20-60%    NA <1% 30-50%     <5% Esophageal 5-90% 40-90% 5-12%  5-60%  NA NA 1% <5% Actual (non-dbSNP) Colorectal 57  5%   47% 26% 75% 25% 12% 2% 30% 98% Pancreatic 24 29%    17% 62% 54%  8% 8% 4%  8% 83% Lung 56  9%    57%  9% 16% 14% 14% 2%  4% 77% Bile Duct15 13%    27% 13% 20% 13% 0 0  7% 67% Gastric 23 17%    22%  4% 35% 17% 4% 4% 0 65% Esophageal 24  4%    46%  4% 33% 17%  4% 0  4% 79%

Some of the frequencies include other genetic alterations than simplysingle base changes and small insertions/deletions such as methylationevents, large homozygous deletions, and copy number changes. Suchalterations would not be reflected in the actual frequency table. Actualfrequencies were derived by sequencing coding and flanking gene regionsfrom 245 patient tissue samples reflecting the spectrum ofaero-digestive cancers. Only non-synonymous and splice site alterationswere tabulated. When specific mutational hot-spot sites were able to beidentified for particular genes, only those sites were analyzed.

The matrix panel includes markers that are present to variable extentacross these tumors so that their aggregate use achieves high overallsensitivity and allows prediction of tumor site using the SITE Model.70% of tumors harbored one or more mutations from the eight gene panel.Some gene mutations, like those associated with p16, are common intumors above the colon but are rare for those in the colon. Mutant K-rasis frequent with colorectal and pancreatic cancers but infrequent in theother cancers. Mutations in EGFR clustered with lung and colorectaltumors and mutations in SMAD4 clustered with stomach and colorectaltumors. Genes such as p53, are commonly mutated across many differenttypes of cancers, but specific mutational locations or types ofmutations within p53 and other genes differ between tumor site (e.g.,Greenman et al, Nature, 446(7132)-153-8 (2007), Soussi and Lozano,Biochem. Biophys. Res. Commun., 331(3):834-42 (2005); Stephens et al.,Nat. Genet., 37(6):590-2 (2005); Sjoblom et al., Science,314(5797):268-74 (2006); Wood et al., Science, 318(5853): 1108-13(2007), and Davies, Cancer Res., 65(17):7591-5(2005)) and can befactored in to the SITE Model to predict tumor site. Single basesubstitutions were the most common type of mutation throughout the paneland those that predicted colorectal tumors included C-G and A-Ttransversions (FIG. 12). Other tumor sites had similarly unique basechange profiles. (Table 4). Insertion/deletions mutations were mostcommon with colorectal tumors, particularly adenomas.

TABLE 4 Specific Base Change Fractions in AD Tumors C > T T > C G > CA > T G > T T > G Tumor (G > A) (A > G) (C > G) (T > A) (C > A) (A > C)Head and Neck 0.38 0.12 0.38 0.12 Esophageal 0.8 0.07 0.13 Lung 0.3 0.110.02 0.13 0.34 0.09 Stomach 0.5 0.25 0.17 0.08 Pancreas 0.41 0.15 0.040.07 0.33 Bile Duct 0.5 0.12 0.25 0.12 CRA 0.34 0.05 0.11 0.11 0.34 0.03CRC 0.4 0.05 0.02 0.24 0.26 0.03

Polypeptide markers found in stool, such as by proteomic approaches, canalso be used to detect aero-digestive neoplasms and predict tumor site.The following was performed to identify and explore candidatepolypeptide markers in stool for the discriminate detection ofpancreatic cancer. Subjects included 16 cases with pancreatic cancer, 10disease controls (colorectal cancer), and 24 healthy controls. Wholestools were collected and frozen promptly in aliquots at −80° C. Thawedaliquots were centrifuged, and the aqueous supernatant from each wasanalyzed. Polypeptides were separated by I-D electrophoresis, excisedfrom gels, and digested for mass spectrometric analysis using anLTQ-Orbitrap. Data outputs were searched using Mascot. Sequest, and X!Tandem programs against an updated

Swissprot database that included all cataloged species. Unique peptidecounts and ratio calculations were performed using Scaffold software.

Median age for pancreatic cancer cases was 67, for colorectal cancercontrols 63, and for healthy controls 62; and male/female distributionswere 9/7, 6/4, and 9/15, respectively. Using shotgun-proteomictechniques on stools, two pancreatic enzymes (carboxypeptidases B andA2) were conspicuous, as unique spectral counts of the former werecommonly elevated with pancreatic cancer and of the latter commonlydecreased. Considered together as the ratio of carboxypeptidaseB/carboxypeptidase A2, pancreatic cancer cases were almost completelyseparated from colorectal cancer and healthy control groups. Medianratios were 0.9, 0.2, and 0.3, respectively. At a specificity cut-offfor the carboxypeptidase B/A2 ratio at 100% (i e, ratios from normalcontrol and colorectal cancer stools all below cut-off), sensitivity forpancreatic cancer was 86 percent (FIG. 2). Only two pancreatic cancerswere misclassified.

These results demonstrate that a stool assay of polypeptide markers canbe a feasible non-invasive approach to the detection pancreatic cancer.These results also demonstrate that multivariable analysis of specificpolypeptide ratios can be used.

In addition, polypeptide markers unique to colorectal neoplasms wereidentified (Table 5). For example, serotransferrin was found in stoolsfrom patients with colorectal cancer but not in those with pancreaticcancer. These markers when considered as part of a matrix panelcontribute both to overall sensitivity for tumor detection and helpdiscriminate colorectal from pancreatic cancer.

TABLE 5 Positive Stool Findings. Carboxypeptidase B/A2* SerotransferrinColorectal Cancer 0 60% Pancreatic Cancer 86% 0 Normal controls 0 0*ratio >0.75 considered positive

Another polypeptide in stool that is pancreatic cancer specific iselastase 3A. Methods and Results demonstrating this are as follows:

Stool Preparation

Samples were collected in phosphate buffered saline and either droppedoff in clinic or mailed in collection tub. Samples were homogenized andfrozen within 72 hours after receipt. Frozen stools were diluted 1:3 w:vin PBS (Roche, Cat#1666789). Diluted stools were stomached in a filterbag (Brinkman, BA6041/STR 177×305 mm) for 60 seconds on control settingand spun at 10.000 rpms for 30 minutes. Following an additional 10minute spin at 14,000 rpm, the supernatant was filtered through a0.45-μm syringe filter and analyzed. Total protein present in stool wasquantitated using a Bradford Protein Assay kit (Pierce).

1-Dimensional Electrophorests

Stool supernatants were diluted 1:1 in Leammli-BME buffer and run on a10.5-14% gradient gel. Vertical slices were cut from 250 kDa to 15 kDaand in-gel digested using methods described elsewhere (e.g., Wilm etal., Nature, 379:466-469 (1996)). Bands were destained, dehydrated,digested in trypsin, extracted, and lyophilized for MS analysis.

Mass Spectrometry

Lyophilized samples were reconstituted and injected with a flow of 500nL/min and a 75 minute gradient from 5-90% 98% acetonitrile. MS wasperformed in data dependent mode to switch automatically between MS andMS² acquisition on the three most abundant ions. Survey scans wereacquired with resolution r=60,000 at 40 m/z using FWHM with a targetaccumulation of 10⁶ counts. An isolation width of 2.5 m/z was applied.Exclusion mass width was 0.6 m/z on low end and 1.5 m/x on high end. Allacquisition and method development was performed using Xcaliber version2.0.

Database Searching all Ms/Ms

Samples were analyzed using Mascot (Matrix Science, London, UK; version2.1.03), Sequest (ThermoFinnigan. San Jose, Calif.; version 27, rev. 12)and X! Tandem (World Wide Web at “thegpm.org”; version 2006.09.15.3).Mascot and X! Tandem were searched with a fragment ion mass tolerance of0.80 Da and a parent ion tolerance of 10.0 PPM. Sequest was searchedwith a fragment ion mass tolerance of 1.00 Da. Nitration of tyrosine wasspecified in Mascot as a variable modification.

Criteria for Polypeptide Identification

Scaffold (version Scaffold-01_06_06, Proteome Software Inc., Portland.Oreg.) was used to validate MS/MS based polypeptide identifications.Peptide identifications were accepted if they could be established atgreater than 95.0% probability as specified by the Peptide Prophetalgorithm (Keller et al. Anal. Chem., 74(20):5383-92 (2002)).Polypeptide identifications were accepted if they could be establishedat greater than 99.0 percent probability and contained at least twoidentified peptides. Polypeptide probabilities were assigned by theProtein Prophet algorithm (Nesvizhskii, Anal. Chem., 75(17):4646-58(2003)). Polypeptides that contained similar peptides and could not bedifferentiated based on MS/MS analysis alone were grouped to satisfy theprinciples of parsimony.

Specific to Elastase SA Ratio Determination

Ratios of elastase 3A were determined using spectral counts for eachpolypeptide. Ratios were determined by dividing the number of uniquepeptides of elastase 3A (determined using a composite ID from databasesearch modules Mascot, XTandem, and Seaquest and compiled in Scaffold)by the number of unique peptides from another polypeptide such aspancreatic alpha-amylase.

Results

The concentration of a specific pancreatic enzyme, elastase 3A, wasconsistently found to be elevated in the fecal supernatant of patientswith pancreatic cancer as compared to normal controls or patients withnon-pancreatic cancer (FIG. 3). These finding indicate that fecalconcentration of elastase 3A is an accurate marker for pancreaticcancer. In addition, the ratio of elastase 3A against other pancreaticenzymes (or other stable fecal polypeptides) was found to be especiallydiscriminant for pancreatic cancer and obviates the need to determineabsolute elastase 3A concentrations (FIG. 4). While mass spectrometrywas used to make these observations, elastase 3A levels and ratiosincluding elastase 3A can be measured using other methods as well.

Example 3 Digital Melt Curve Assay for Scanning Mutations

A sensitive, rapid, and affordable method for scanning mutations inbodily fluids at high-throughput was developed. A melt curve assay is apost-PCR technique that can be used to scan for mutations in PCRamplicons. Mutations in PCR products can be detected by changes in theshape of the melting curve (heterozygote from mutant sample) compared toa reference sample (homozygote from wild-type sample) (FIG. 5). Meltcurve assay can scan all mutations in a DNA fragment <400 bp in lessthan 10 minutes, rather than individually targeting single mutations.Regular melt curve assays can detect mutations down to a limit of 5%mutant:wild-type and, thus, are not sensitive enough to detect mutationsin many biological samples. For instance, in stool, an analyticalsensitivity of 1% or less is required in order to detect precancerouspolyps or small early stage cancers. Importantly, a quantitative scorecan be given to density of target mutations (FIG. 6).

Digital PCR can augment the sensitivity of PCR to detect low abundancemutations. Gene copies can be diluted and distributed into 96 wells of aplate to increase the percentage of mutant copy to wild-type copies incertain wells. For example, if a stool DNA sample contains only 1% ofmutant BRAF copies compared to wild-type copies, distributing 300 copiesof BRAF gene into a 96-well plate can lead to three wells with anaverage mutant ratio of 33 percent (1:3). After PCR amplification, thesethree wells with mutant copies can be detected by sequencing or otherapproaches. Since digital PCR requires PCR on a whole 96-well plate and96 sequencings (or other approaches) for each target, it can be slow andcostly.

The concept of digital melt curve assay is to combine the scanningability and speed of high resolution melt curve assay with thesensitivity of digital PCR. Miniaturizing and automating this technologydramatically lowers per assay cost and achieves high-throughputnecessary for population screening.

The following procedure was used to perform a digital melt curve assay.To prepare a DNA sample, gene target fragments (e.g., BRAF. K-ras. APC,p16, etc.) were captured from stool DNA using a sequence-specificcapture method and were quantified with real-time PCR. About 200 to 2000gene copies were mixed in tube with all PCR reagents. An average of 2 to20 copies (variable) were distributed to each well of a 96-well plate.PCR amplification was performed using specific primers on the plate(e.g., one target per plate). Final concentrations of PCR mastermix forDigital Melt Curve assays in a 96-well plate (500 L dispersed to 96wells with each well containing 5 μL) were as follows: 2× pfxamplification buffer (Invitrogen), 0.3 mM each dNTP, 200 nM forwardprimer, 200 nM reverse primer, 1 mM MgSO₄, 0.02 unit/μL Platinum® pfxpolymerase (Invitrogen), and 0.1 unit/μL LcGreen+ dye (Idaho Tech). Ahigh resolution melt curve assay was used to identify the wells withmutant copies. Sequencing was optionally performed to confirm 1 to 2representative wells.

In some cases, emulsion PCR can be used in place of digital PCR. In suchcases, each lipid drop can become a tiny PCR reactor of one singlemolecule of gene.

TABLE 6 Sequence Specific Capture Probes and DNA Primer Capture TargetProbe/ SEQ Gene Region Primer Oligo Sequence (5′→3′) ID No. KRASCondons 12/13 Probe GTGGACGAATATGATCCAACAATAGAGGTAAATCTTG  1Condons 12/13 Primer 1 AGGCCTGCTGAAAATGACTG  2 TTGTTGGATCATATTCGTCCAC  3Condons 12/13 Primer 2 TAAGGCCTGCTGAAAATGAC  4 ATCAAAGAATGGTCCTGCAC  5Condons 12/13 Primer 3 CGTCTGCAGTCAACTGGAATTT  6 TGTATCGTCAAGGCACTCTTGC 7 Condons 12/13 Primer 4 CTTAAGCGTCGATGGAGGAG  8 TTGTTGGATCATATTCGTCCAC 3 BRAF V600E Probe CCAGACAACTGTTCAAACTGATGGGACCCACTCCATC  9 V600EPrimer CCACAAAATGGATCCAGACA 10 TGCTTGCTCTGATAGGAAAATG 11 APC MCR Probe 1CAGATAGCCCTGGACAAACCATGCCACCAAGCAGAAG 12 MCR Probe 2TTCCAGCAGTGTCACAGCACCCTAGAACCAAATCCAG 13 MCR Probe 3ATGACAATGGGAATGAAACAGAATCAGAGCAGCCTAAAG 14 Condons 1286-1346 Primer 1TTCATTATCATCTTTGTCATCAGC 15 CGCTCCTGAAGAAAATTCAA 16 Condons 1346-1367Primer 2 TGCAGGGTTCTAGTTTATCTTCA 17 CTGGCAATCGAACGACTCTC 18Condons 1394-1480 Primer 3 CAGGAGACCCCACTCATGTT 19TGGCAAAATGTAATAAAGTATCAGC 20 Condons 1450-1489 Primer 4CATGCCACCAAGCAGAAGTA 21 CACTCAGGCTGGATGAACAA 22 Condon 1554 PrimersGAGCCTCGATGAGCCATTTA 23 TCAATATCATCATCATCTGAATCATC 24 102457delCPrimer 6 GTGAACCATGCAGTGGAATG 25 ACTTCTCGCTTGGTTTGAGC 26 102457delCPrimer 7 CAGGAGACCCCACTCATGTT 19 CATGGTTTGTCCAGGGCTAT 27 102457delCPrimer 8 GTGAACCATGCAGTGGAATG 25 AGCATCTGGAAGAACCTGGA 28 TP53 Exon 4Probe AAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAGA 29 Exon 4 PrimerCCCTTCCCAGAAAACCTACC 30 GCCAGGCATTGAAGTCTCAT 31 Exon 5 ProbeCATGGCCATCTACAAGCAGTCACAGCACATGACGGAG 32 Exon 5 PrimerCACTTGTGCCCTGACTTTCA 33 AACCAGCCCTGTCGTCTCT 34 Exon 6 ProbeAGTGGAAGGAAATTTGCGTGTGGAGTATTTGGATGAC 35 Exon 6 PrimerCAGGCCTCTGATTCCTCACT 36 CTTAACCCCTCCTCCCAGAG 37 Exon 7 ProbeATGTGTAACAGTTCCTGCATGGGCGGCATGAACCGGA 38 Exon 7 PrimerCTTGGGCCTGTGTTATCTCC 39 GGGTCAGAGGCAAGCAGA 40 Exon 8 ProbeCGCACAGAGGAAGAGAATCTCCGCAAGAAAGGGGAGC 41 Exon 8 PrimerGGGAGTAGATGGAGCCTGGT 42 GCTTCTTGTCCTGCTTGCTT 43

Example 4 Sensitive Detection of Mutations Using a Digital Melt CurveAssay

The following was performed to develop a quantitative method forscanning gene mutations and to evaluate the sensitivity of thequantitative method for detecting target mutations in stool. A digitalmelt curve assay was designed by combining digital PCR to a modifiedmelt curve assay. Target genes in low concentration were PCR amplifiedwith a saturated DNA dye, LcGreen+, in a 96-well plate. Each wellcontained a small number of gene copies, which allowed highmutation/wild-type ratios in some wells that were then detected by meltcurve scanning using a LightScanner. Mutations were scored based on thenumber of wells containing mutant copies in a 96-well plate. To testsensitivity, mutant genes were spiked into a wild-type pool at 0.1, 0.5,1, 5, and 10% dilutions, and analyzed using digital melt curve assaywith 250-1000 gene copies per 96-well plate. This method was thenapplied in the stool detection of APC, p53, K-ras, and BRAF mutationsfrom 48 patients known to have mutations in one of these genes inmatched tumor tissue. Subjects included 9 patients with pancreaticcancer, 31 with colorectal cancer, and 8 with colorectal adenoma >1 cm.All mutations detected by digital melt curve were further confirmed bySanger sequencing.

The digital melt curve assay detected as few as 0.1% mutant copies foramplicons <350 bp using one 96-well plate (FIG. 7), compared to thedetection limit for regular melt curve of ≧5 percent. Each mutationscanning took 8-10 minutes with this manual approach. Mutations of APC,p53. K-ras, and BRAF genes were all successfully scanned with digitalmelt curve in quantitative fashion. Tissue-confirmed mutations weredetected from matched stools in 88 percent (42148) of patients withgastrointestinal neoplasms, including 89 percent with pancreatic cancer,90 percent with colorectal cancer, and 75 percent with colorectaladenoma >1 cm.

These results demonstrate that a digital melt curve assay can be ahighly sensitive approach for detecting mutations in stool, and that ithas potential for diagnostic application with both upper and lowergastrointestinal neoplasms.

Example 5 Using a Digital Melt Curve Assay to Detect Adenomas

Archived stools were used to evaluate a digital melt curve assay of DNAmarkers for detection of advanced adenomas and to compare the accuracyof the digital melt curve assay with that of occult blood testing and acommercial DNA marker assay method (EXACT Sciences). Average risksubjects collected stools without a preservative buffer and mailed themto central processing laboratories for banking and blinded stool testingby Hemoccult. HemoccultSENSA, and DNA marker assay. All subjectsunderwent a colonoscopy, and tissue from advanced adenomas was archived.Archival stools were selected from the 27 patients with a colorectaladenoma >1 cm found to harbor mutant K-ras on tissue analyses and fromthe first 25 age and gender matched subjects with normal colonoscopy.Standard methods were used to extract crude DNA from fecal aliquots, andK-ras gene was enriched by sequence capture. Mutations in the K-ras genewere quantified by a digital melt curve assay based on the number ofwells containing mutant gene copies in a 96-well plate and confirmed bysequencing.

Median age with adenomas was 67 and controls 71; and males/females were12/15 and 13/14, respectively. Median adenoma site was 1.5 cm (range 1-3cm). Based on a cut-off of >3 wells with mutant K-ras, the digital meltcurve assay yielded an overall sensitivity of 59 percent for adenomaswith a specificity of 92 percent; sensitivity for adenomas >2 cm was 80percent (8/10) and for those <2 cm was 47 percent (8/17), p=0.1. Inthese same stools, overall adenoma detection rates were 7 percent byHemoccult, 15 percent by HemoccultSENSA, and 26 percent by the EXACTSciences K-ras assay (p<0.05 for each vs. digital melt curve) (FIG. 8).Respective specificities were 92 percent, 92 percent, and 100 percent.

These results demonstrate that an analytically-sensitive digital meltcurve assay method can be used to detect a majority of advancedcolorectal adenomas and improve yield over current stool testapproaches.

Example 6 Short DNA as a Cancer Marker

Free human DNA is present in all human stools and arises from cells shed(exfoliated) from the normal surface (mucosa) of the aero-digestivetract (mouth/throat, lungs, and all digestive organs) and from tumors orother lesions that may be present. It has been generally accepted that“long DNA” in stool reflects that presence of colorectal and otheraero-digestive tumors, in that cells exfoliated from cancers do notundergo typical cell death (apoptosis) which would shorten DNA.Specifically, because DNA from apoptotic cells would be broken down tofragment lengths shorter than 100 bp, long DNA was defined as beinglonger than 100 bp. Indeed, levels of long DNA were elevated in stoolsfrom patients with colorectal and other cancers as compared to thosefrom healthy controls (Zou et al., Cancer Epidemiol. Biomarkers Prev.,15: 115 (2006): Ahlquist et al., Gastroenterology, 119:1219 (2000); andBoynton et al. Clin. Chem., 49:1058 (2003)). As such, long DNA in stoolcan serve as a marker for colorectal and other tumors.

“Short DNA” (i.e., <100 bp in length), however, was found to be as ormore discriminant than long DNA as a tumor marker in stool for detectionof both colorectal (FIG. 9) and pancreatic (FIG. 10) neoplasia.

Briefly, methods and materials similar to those described elsewhere wereused to detect short DNA present in stool samples (Zou et al., CancerEpidemiol, Biomarkers Prev., 15(6): 1115 (2006)). Total DNA wasextracted by isopropanol precipitation from 19 blinded stool samples, 9pancreatic adenocarcinoma, and 10 age/gender matched normals. The DNApellets were taken up in 8 mL of 10-fold diluted TE, pH 8. The Alusequence consists of conserved regions and variable regions. In theputative consensus Alu sequence, the conserved regions are the 25-bpspan between nucleotide positions 23 and 47 and the 16-bp span betweennucleotide positions 245 and 260. Although primers can be designed inany part of the Alu sequences, for more effectively amplifying Alusequences, the PCR primers are preferably completely or partially (atleast the 3′-regions of the primers) located in the conserved regions.Primers specific for the human Alu sequences were used to amplifyfragments of differing lengths inside Alu repeats. The sequences were asfollows:

Amplicon size Primer Sequences 245 bp Forward Primer:5′-ACGCCTGTAATCCCAGCACTT-3′ (SEQ ID NO: 44) Reverse Primer:5′-TCGCCCAGGCTGGAGTGCA-3′ (SEQ ID NO: 45) 130 bp Forward Primer:5′-TGGTGAAACCCCGTCTCTAC-3′ (SEQ ID NO: 46) Reverse Primer:5′-CTCACTGCAACCTCCACCTC-3′ (SEQ ID NO: 47)  45 bp Forward Primer:5′-TGGTGAAACCCCGTCTCTAC-3′ (SEQ ID NO: 46) Reverse Primer:5′-CGCccGGCTAATTTTTGTAT-3′ (SEQ ID No: 48)

Stool DNA was diluted 1:5 with Ix Tris-EDTA buffer (pH 7.5) for PCRamplification. Tris-EDTA buffer-diluted stool DNA (1 μL) was amplifiedin a total volume of 25 μL containing Ix iQ SYBR Green Supermix(Bio-Rad, Hercules, Calif.), 200 nmol/L each primer under the followingconditions: 95° C. for 3 minutes followed by 40 cycles of 95° C., 60°C., and 72° C. for 30 seconds each. A standard curve was created foreach plate by amplifying 10-fold serially diluted human genomic DNAsamples (Novagen. Madison, Wis.). Melting curve analysis was made aftereach PCR to guarantee that only one product was amplified for allsamples.

Amplification was carried out in 96-well plates in an iCycler (Bio-Rad).Each plate consisted of stool DNA samples and multiple positive andnegative controls. Each assay was done in duplicate.

The following was performed to compare long DNA (245 bp) and short (45bp) human DNA in stool for detection of upper and lower GI neoplasms,and to assess the effect of GI tumor site on human DNA levels in stool.Subjects included 33 patients with colorectal cancer, 20 with colorectaladenomas >1 cm, 13 with pancreatic cancer, and 33colonoscopically-normal controls. Subjects added a preservative bufferto stools at time of collection to prevent post-defecation bacterialmetabolism of DNA, and stools were frozen within 8 hours at −80° C.Using a validated quantitative assay for human DNA (Zou et al. EpidemiolBiomarker. Prev., 15:1115 (2006)), 245 bp and 45 bp Alu sequences wereamplified from all stools in blinded fashion. Sensitivities for long andshort DNA were based on 97 percent specificity cut-offs.

Age medians were 60, 66, 69, and 62 for colorectal cancer, colorectaladenoma, pancreatic cancer, and control groups, respectively; andmale/female distributions were 22/11, 9/11, 9/4, 11/21, respectively. Instools from neoplasm and control groups, amplification products werequantitatively greater for short DNA versus long DNA Respectivesensitivities by long and short DNA were 66 percent and 62 percent withthe 29 distal colorectal neoplasms, 46 percent and 46 percent with the24 proximal colorectal neoplasms, and 15 percent and 31 percent (p=0.16)with the 13 pancreatic cancers. By Wilcoxan Rank-Sum test, effect ofneoplasm site on detection rates was significant for both long DNA(p=0.004) and short DNA (p=0.02). Among colorectal neoplasms, respectivesensitivities by long and short DNA were 48 percent and 52 percent withlesions <3 cm, 63 percent and 63 percent with those >3 cm, 64 percentand 61 percent with cancers, and 35 percent and 45 percent withadenomas.

These results demonstrate that short and long DNA can be comparablysensitive for stool detection of GI neoplasms. However, detection ratesvary with tumor site, being greatest with the most distal lesions andlowest with the most proximal ones. These results were consistent withsubstantial luminal degradation of DNA exfoliated from more proximal GIneoplasms.

It was also demonstrated that mutant gene markers in stool can bedetected to a greater extent if amplicon size is less than 70 bp,consistent with luminal degradation. Thus, short DNA can serve as amarker per se and as the target size for mutation detection.

Example 7 Use of Fecal Methylated BMP3 as a Neoplasia Marker

Stools from patients with colorectal tumors were found to containsignificantly elevated amounts of methylated BMP3 gene copies, but thosefrom normal individuals were found to contain none or only traceamounts. When fecal methylated BMP3 was assayed with an appropriateamplification method, colorectal cancers and premalignant adenomas werespecifically detected (FIG. 1). Fecal methylated BMP3 detected a higherpercentage of proximal colon tumors than distal tumors, so it can becombined with markers for distal colorectal tumors to createcomplementary marker panels. Fecal methylated BMP3 was very specificwith few false-positive reactions.

Similar results can be obtained using other genes and methods such asthose described elsewhere (Zou et al., Cancer Epidemiol BiomarkersPrev., 16(12):2686 (2007)).

Example 8 Detecting Aero-Digestive Cancers by Stool DNA Testing

Tissue samples from patients with confirmed aero-digestive tumors wereextracted and sequenced to assess the presence or absence of somaticgene alterations. Germline DNA from the same patients were used ascontrols. Once an alteration was confirmed, a matched stool sample wastested for that alteration. Two separate methods were utilized to detectthe mutation in stool: Allele specific PCR and digital melt curveanalysis. For both methods, we focused on amplifying the shortestfragments possible (>100 bp) that have been shown to contain higherlevels of the mutant sequence.

Digital Melt Curve (DMC)

We studied 138 patients (69 cases with a GI neoplasm and 69age/sex-matched asymptomatic controls with normal colonoscopy) by first,identifying a mutation in neoplasm tissue, and then determining if thatspecific mutation could be detected in stool from that individual.Stools were collected with a stability buffer and frozen at −80° C.until assayed.

Genes commonly mutated in GI neoplasms (TP53, KRAS, APC, CDH1, CTNNB1,BRAF, SMAD4, and P16) were sequenced from DNA extracted from tumortissue, to identify a target mutation for each case. Target genes wereisolated by hybrid capture (Table 7) and the tissue-confirmed somaticmutations were assayed in stool by the digital melt curve method, asdescribed in Example 1. Mutations detected in stool were confirmed bysequencing. Assays were performed blinded.

TABLE 7 Sequence Specific Capture Probes andPrimers for AD Cancer Mutation Detection SEQ SEQ ANTISENSE SEQ MUTATIONID SENSE PRIMER  ID PRIMER 2 ID IN TISSUE CAPTURE PROBE No. 1 (5′ TO 3′)No. (5′ TO 3′) No. 12487C>CT:167Q>Q/X ATGGCCATCTACAAGCAGTCAT  49AGTACTCCCCTGC 128 CTCACAACCTCCG 169 AGCACATGACGGAGGTTGT CCTCAAC TCATGTG102447_102450het_delTGGT AGAGTGAACCATGCAGTGGAAA  50 TTTGAGAGTCGTT 129CATGGTTTGTCCA  27 AGTGGCATTATAAGCCC CGATTGC GGGCTAT 12410G>GA, 141C>C/YTTTGCCAACTGGCCAAGACCTA  51 AGTACTCCCCTGC 128 CTCCGTCATGTGC 170CCCTGTGCAGCTGTG CCTCAAC TGTGACT 102678het_delA CAGATGCTGATACTTTATTACT 52 TCCAGGTTCTTCC 130 CACTCAGGCTGGA  22 TTTGCCACGGAAAGTACT AGATGCTTGAACAA 102594_102598het_delAGAGA AAAGCACCTACTGCTGAAAGAG  53AGCTCAAACCAAG 131 AGCATCTGGAAGA  28 AGTGGACCTAAGCAAG CGAGAAG ACCTGGA102644_102646het_insG ATGCTGCAGTTCAGAGGGGTCC  54 GGACCTAAGCAAG 132CACTCAGGCTGGA  22 AGGTTCTTCCAGATGC CTGCAGTA TGAACAA102594_102595het_delAG TAAAGCACCTACTGCTGAAAAG  55 AGCTCAAACCAAG 131AGCATCTGGAAGA  28 AGAGAGTGGACCTAAGCAAG CGAGAAG ACCTGGA 102106het_delTCACAGGAAGCAGATTCTGCAAT  56 CAGACGACACAGG 133 TGCTGGATTTGGT 171ACCCTGCAAATAGCA AAGCAGA TCTAGGG 102442het_delT TTCAGAGTGAACCATGCAGGGA 57 TTTGAGAGTCGTT 129 CATGGTTTGTCCA  27 ATGGTAAGTGGCATTAT CGATTGCGGGCTAT apc 102494C>CT; 1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58GTGAACCATGCAG  25 AGCTGTTTGAGGA 172 ATGCCACCAAG TGGAATG GGTGGTGapc 102557C>CT; 1450R>R/X CTCAAACAGCTCAAACCAAGTG  59 ACCACCTCCTCAA 134GCAGCTTGCTTAG 173 AGAAGTACCTAAAAATAAA ACAGCTC GTCCACT apc 102140het_delAAGCAGAAATAAAAGAAAAGTTG  60 CAGACGACACAGG 133 TGCTGGATTTGGT 171GAACTAGGTCAGCTGA AAGCAGA TCTAGGG apc 102494C>CT; 1429Q>Q/XTCCAGATAGCCCTGGATAAACC  58 GTGAACCATGCAG  25 AGCTGTTTGAGGA 172ATGCCACCAAG TGGAATG GGTGGTG apc 102554het_delA CTCAAACAGCTCAAACCAGCGA 61 CATGCCACCAAGC  21 GCAGCTTGCTTAG 173 GAAGTACCTAAA AGAAGTA GTCCACTtp53 E5 12647A>AG: 193H>H/R TCTGGCCCCTCCTCAGCGTCTT  62 CAGGCCTCTGATT  36ACACGCAAATTTC 174 ATCCGAGTGGAAG CCTCACT CTTCCAC tp53 E5 12742G>GACCTATGAGCCGCCTGAGATCTG  63 CATAGTGTGGTGG 135 AACCACCCTTAAC 175GTTTGCAACTGGG TGCCCTA CCCTCCT tp53 E5 12706C>CT:213R>R/XATGACAGAAACACTTTTTGACA  64 GTGGAAGGAAATT 136 CAGTTGCAAACCA 176TAGTGTGGTGGTG TGCGTGT GACCTCA tp53 E412712A>AG:215S>S/GGAAACACTTTTCGACATGGTGT  65 GTGGAAGGAAATT 136 CAGTTGCAAACCA 176GGTGGTGCCCTAT TGCGTGT GACCTCA tp53 E4 12388T>TC:134F>F/LCTGCCCTCAACAAGATGCTTTG  66 TGTTCACTTGTGC 137 GCAGGTCTTGGCC 177CCAACTGGCCAAG CCTGACT AGTTG tp53 E3 11606G>GA:125T>T/TAAGTCTGTGACTTGCACAGTCA  67 GTCTGGGCTTCTT 138 GCCAGGCATTGAA  31GTTGCCCTGAGGG GCATTCT GTCTCAT tp53 E6 13379C>CT:248R>R/WGCATGGGCGGCATGAACTGGAG  68 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178GCCCATCCTCACC ACCACCA GGTGAGG tp53 12E3 11326A>AC TCTTTTCACCCATCTACCGTCC 69 ACCTGGTCCTCTG 140 GGGGACAGCATCA 179 (splice site) CCCTTGCCGTCCCACTGCTC AATCATC tp53 E6 13412G>GT:259D>D/Y CCATCATCACACTGGAATACTC  70CCTCACCATCATC 141 GGGTCAGAGGCAA  40 CAGGTCAGGAGCC ACACTGG GCAGAtp53 E4 12449G>GT:154G>G/V ACCCCCGCCCGTCACCCGCGTC  71 GTGCAGCTGTGGG 142CTCCGTCATGTGC 170 C TTGATT TGTGACT tp53 E7 13872G>GT,298E>E/XGGAACAGCTTTGAGGTGTGTGT  72 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43TTGTGCCTGTCCT CGCAAGA CTTGCTT APC 102843C>CG:1545S>S/XTCAGAGCAGCCTAAAGAATGAA  73 ATGCCTCCAGTTC 144 TTTTTCTGCCTCT 180ATGAAAACCAAGAGAAA AGGAAAA TTCTCTTGG tp53 E4 12392G>GT, 135C>C/FCCTCAACAAGATGTTTTTCCAA  74 TGCCCTGACTTTC 145 CTGCACAGGGCAG 181CTGGCCAAGACCT AACTCTGT GTCTT APC 102557C>CT: 1450R>R/XCTCAAACAGCTCAAACCAAGTG  59 ACCACCTCCTCAA 134 GCAGCTTGCTTAG 173AGAAGTACCTAAAAATAAA ACAGCTC GTCCACT tp53 E7 13819G>T:280R>ICCTGTCCTGGGATAGACCGGCG  75 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 CACACAGCTT TCCTCTG tp53 13E4 11326A>AC TCTTTTCACCCATCTACCGTCC  69ACCTGGTCCTCTG 140 GGGGACAGCATCA 179 (splice site) CCCTTGCCGTCCC ACTGCTCAATCATC tp53 E7 13412G>GT:259D>D/Y CCATCATCACACTGGAATACTC  70CCTCACCATCATC  41 GGGTCAGAGGCAA  40 CAGGTCAGGAGCC ACACTGG GCAGAtp53 E5 12449G>GT:154G>G/V ACCCCCGCCCGTCACCCGCGTC  71 GTGCAGCTGTGGG 142CTCCGTCATGTGC 170 C TTGATT TGTGACT tp53 E8 13872G>Gt:298E>E/XGGAGCCTCACCACTAGCTGCCC  76 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43 CCAGGCGCAAGA CTTGCTT tp53 E8 13813C>CG,278P>P/R GTGTTTGTGCCTGTCGTGGGAG  77CTACTGGGACGGA 146 GCGGAGATTCTCT 182 AGACCGGCG ACAGCTT TCCTCTGtp53 13851A>AT,291K>K/X GGAAGAGAATCTCCGCTAGAAA  78 GCGCACAGAGGAA 147TTCTTGTCCTGCT 183 GGGGAGCCTCA GAGAATC TGCTTACCsmad4 E2 19049G>GA, 18118A>A/A GTTAAATATTGTCAGTATGCAT  79 AGGTGGCCTGATC148 TGGATTCACACAG 184 TTGACTTAAAATGTGATAG TTCACAA ACACTATCACAtp53 E8 13777G>GA:266G>G/E TAGTGGTAATCTACTGGAACGG  80 TTTCCTTACTGCC 149CACAAACACGCAC 185 AACAGCTTTGAGGTG TCTTGCTTC CTCAAAGtp53 E6 12653T>TC:195T>I/T CCTCCTCAGCATCTTACCCGAG  81 CAGGCCTCTGATT  36ACACGCAAATTTC 174 TGGAAGGAAAT CCTCACT CTTCCAC tp53 E7 13379C>CT:248R>R/WGCATGGGCGGCATGAACTGGAG  68 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178GCCCATCCTCACC ACCACCA GGTGAGG tp53 E6 12647A>AG:193H>H/RTCTGGCCCCTCCTCAGCGTCTT  62 CAGGCCTCTGATT  36 ACACGCAAATTTC 174ATCCGAGTGGAAG CCTCACT CTTCCAC tp53 E6 12712A>AG:215S>S/GGAAACACTTTTCGACATGGTGT  65 GTGGAAGGAAATT 136 CAGTTGCAAACCA  76GGTGGTGCCCTAT TGCGTGT GACCTCA tp53 E8 13872G>GT,298E>E/XGGAGCCTCACCACTAGCTGCCC  76 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43 CCAGGCGCAAGA CTTGCTT tp53 E7 13370G>GA:245G>G/S AGTTCCTGCATGGGCAGCATGA  82TGGCTCTGACTGT 139 CCAGTGTGATGAT 178 ACCGGAGGC ACCACCA GGTGAGGtp53 E4 11580het_delG CTGGGCTTCTTGCATTCTGGAC  83 CCCTTCCCAGAAA  30ACTGACCGTGCAA 186 AGCCAAGTCTGTGA ACCTACC GTCACAGtp53 E5 12524A>AG,179H>H/R TGCCCCCACCGTGAGCGCTGC  84 TGGCCATCTACAA 150CTGCTCACCATCG 187 GCAGTCA CTATCTG >tp53 E6 12661G>GT,198E>E/XTCAGCATCTTATCCGAGTGTAA  85 CAGGCCTCTGATT  36 CCAAATACTCCAC 188GGAAATTTGCGTGTGGA CCTCACT ACGCAAA tp53 E8 13872G>GT:298E>E/XGGAGCCTCACCACTAGCTGCCC  76 GGAAGAGAATCTC 143 GCTTCTTGTCCTG  43 CCAGGCGCAAGA CTTGCTT apc 102494C>CT;1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58CAGGAGACCCCAC  19 TGGCAAAATGTAA  20 ATGCCACCAAG TCATGTT TAAAGTATCAGCapc 102557C>CT;1450R>R/X CTCAAACAGCTCAAACCAAGTG  59 CAGGAGACCCCAC  19TGGCAAAATGTAA  20 AGAAGTACCTAAAAATAAA TCATGTT TAAAGTATCAGCapc 102140het_delA AGCAGAAATAAAAGAAAAGTTG  60 TTCATTATCATCT  15CGCTCCTGAAGAA  16 GAACTAGGTCAGCTGA TTGTCATCAGC AATTCAAapc 102494C>CT;1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58 CAGGAGACCCCAC  19TGGCAAAATGTAA  20 ATGCCACCAAG TCATGTT TAAAGTATCAGCapc 102134G>GT;1309E>E/X TGCAAATAGCAGAAATAAAATA  86 TTCATTATCATCT  15CGCTCCTGAAGAA  16 AAAGATTGGAACTAGGTCA TTGTCATCAGC AATTCAAapc 102554het_delA CTCAAACAGCTCAAACCAGCGA  61 CAGGAGACCCCAC  19TGGCAAAATGTAA  20 GAAGTACCTAAA TCATGTT TAAAGTATCAGC apc 102852het_insACTAAAGAATCAAATGAAAAACC  87 GAGCCTCGATGAG  23 TCAATATCATCAT  24AAGAGAAAGAGGCAGAA CCATTTA CATCTGAATCATC Kras 5571G>GA;12G>G/DGTGGTAGTTGGAGCTGATGGCG  88 AGGCCTGCTGAAA   2 TTGTTGGATCATA   3TAGGCAAGAGT ATGACTG TTCGTCCAC tp53 E4 12392G>GA;135C>C/YCCTCAACAAGATGTTTTACCAA  89 TGTTCACTTGTGC 137 GCAGGTCTTGGCC 177CTGGCCAAGACCT CCTGACT AGTTG tp53 E5 12655C>CT;196R>R/XCCTCCTCAGCATCTTATCTGAG  90 CAGGCCTCTGATT  36 ACACGCAAATTTC 174TGGAAGGAAATTTGC CCTCACT CTTCCAC tp53 E6 13350G>GA;238C>C/YTCCACTACAACTACATGTATAA  91 TGGCTCTGACTGT 139 CCAGTGTGATGAT 178CAGTTCCTGCATGGG ACCACCA GGTGAGG tp53 E6 13420G>GA CACTGGAAGACTCCAGATCAGG 92 CCTCACCATCATC 141 GGGTCAGAGGCAA  40 AGCCACTTGCC ACACTGG GCAGAtp53 E5 12712A.AG;215S>S/G GAAACACTTTTCGACATGGTGT  65 GTGGAAGGAAATT 136CAGTTGCAAACCA 176 GGTGGTGCCCTAT TGCGTGT GACCTCA Kras 5571G>GA;12G>G/DGTGGTAGTTGGAGCTGATGGCG  88 AGGCCTGCTGAAA   2 TTGTTGGATCATA   3TAGGCAAGAGT ATGACTG TTCGTCCAC P16(ink4a) E1 19638A>ATGGAGAGGGGGAGTGCAGGCAGC  93 AGCCAGTCAGCCG 151 GAGGGGCTGGCTG 189 GGG AAGGGTC P16(ink4a) E2 23353G>GT;447D>DY CCCAACTGCGCCTACCCCGCCA  94CACCCTGGCTCTG 152 GGGTCGGGTGAGA 190 CTC ACCAT GTGGP16(ink4a) E1 19638A>AT GGAGAGGGGGAGTGCAGGCAGC  93 AGCCAGTCAGCCG 151GAGGGGCTGGCTG 189 GGG AAGG GTC p16(ink4a) E2 23402het_delT_GCCCGGGAGGGCTCCTGGACAC  95 GACCCCGCCACTC 153 CAGCTCCTCAGCC 191 GCTG TCACAGGTC p16(ink4a) E2 23403C>CA;484F>F/ GCCCGGGAGGGCTTACTGGACA  96GACCCCGCCACTC 153 CAGCTCCTCAGCC 191 CGCTGGT TCAC AGGTCctnnb1 25541het_delT CAATGGGTCATATCACAGATTC  97 ATATTTCAATGGG 154TCAAATCAGCTAT 192 TTTTTTTTAAATTAAAGTAACA TCATATCACAG AAATACGAAACAcdh 1 E9 76435het_delA TCTTATCTCAAAAGAACAACAA  98 GCCATGATCGCTC 155TCTCAGGGGGCTA 193 AAAAGAGGAATCCTTTAG AAATACA AAGGATTcdh 1 E1 743_744het_ GCGCCCAGCCCTGCGCCCATTC  99 ACTTGCGAGGGAC 156GAAGAAGGGAAGC 194 insAGCCCTGCGCCCA CTC GCATT GGTGACcdh 1 E13 8685386854het_insA AAGTAAGTCCAGCTGGCAAAGT 100 CATTCTGGGGATT157 GGAAATAAACCTC 195 GACTCAGCCTTTGACTT CTTGGAG CTCCATTTTTcdh 1 E14 91472C>CT;751N>N/N AGGATGACACCCGGGACAATGT 101 CTGTTTCTTCGGA158 CCGCCTCCTTCTT 196 TTATTACTATGATGAAG GGAGAGC CATCATAcdh 1 E15 92868_92896hct_ TTTTTTCTCCAAAGGACTGACG 102 TTCCTACTCTTCA 159TGCAACGTCGTTA 197 delTTGACTTGAGCCAGCTGCACAGGGGCCTG CTCGGCCTGAAGTGTTGTACTTCAACC CGAGTCA cdh 1 E4 71669*het_delA CAAGCAGAATTGCTCACTTTCC 103CGTTTCTGGAATC 160 GCAGCTGATGGGA 198 CAACTCCTCTCC CAAGCAG GGAATAAcdh 1 E7 74926G>GA;289A>A/T GGTCACAGCCACAGACACGGAC 104 CCAGGAACCTCTG 161TGAGGATGGTGTA 199 GATGATGTGAA TGATGGA AGCGATGcdh 1 E1 736_742het_delTGCGCCC AGCCCTGCGCCCCTTCCTCTCC 105 ACTTGCGAGGGAC156 GAAGAAGGGAAGC 194 CG GCATT GGTGAC p16(ink4a)E1 19638A>ATGGAGAGGGGGAGTGCAGGCAGC  93 AGCCAGTCAGCCG 151 CTCACAACCTCCG 169 GGG AAGGTCATGTG tp53 E4 12365A>AG;126Y>Y/C TTCCTCTTCCTACAGTGCTCCC 106CACTTGTGCCCTG  33 GCCAGTTGGCAAA 200 CTGCCCTCAAC ACTTTCA ACATCTtp53 E4 12548G>GA TGCTCAGATAGCGATGATGAGC 107 CACATGACGGAGG 162AACCAGCCCTGTC  34 AGCTGGGGCTG TTGTGAG GTCTCTp16(ink4a)E1 19810T>TG;491I>I/S GGTCGGAGGCCGAGCCAGGTGG 108 TTCCAATTCCCCT163 CCCAACGCACCGA 201 GTAGA GCAAA ATAGT tp53 E7 13757G>GAGCTTCTCTTTTCCTATCCTAAG 109 GGGACAGGTAGGA 164 AGCTGTTCCGTCC 202TAGTGGTAATCTACTGG CCTGATTT CAGTAGA tp53 E7 13815G>GC;279G>G/RTTGTGCCTGTCCTCGGAGAGAC 110 CTACTGGGACGGA 146 GCGGAGATTCTCT 182 CGGCGACAGCTT TCCTCTG tp53 E7 13816G>GA;279G>G/E TGTGCCTGTCCTGAGAGAGACC 111CTACTGGGACGGA 146 GCGGAGATTCTCT 182 GGCGC ACAGCTT TCCTCTGtp53 E5 12365A>AC,126Y>Y/S TTCCTCTTCCTACAGTCCTCCC 112 CACTTGTGCCCTG  33GCCAGTTGGCAAA 200 CTGCCCTCAAC ACTTTCA ACATCT tp53 E5 12491A>AT,168H>HLTCTACAAGCAGTCACAGCTCAT 113 TGGCCATCTACAA 150 CTGCTCACCATCG 187GACGGAGGTTGTGGA GCAGTCA CTATCTG 113 TGGCCATCTACAA 150 TCACCATCGCTAT 203GCAGTCA CTGAGCA 113 TGGCCATCTACAA 150 AACCAGCCCTGTC  34 GCAGTCA GTCTCTkras 5570G>GC,12G>G/R GTGGTAGTTGGAGCTGATGGCG  88 AGGCCTGCTGAAA   2TTGTTGGATCATA   3 TAGGCAAGAGT ATGACTG TTCGTCCACtp53 E7 13370G>GA,245G>G/S AGTTCCTGCATGGGCAGCATGA  82 TGGCTCTGACTGT 139CCAGTGTGATGAT 178 ACCGGAGGC ACCACCA GGTGAGG apc 102864_102865het_delAGAAATGAAAACCAAGAGAAAGGC 114 TGACAATGGGAAT 165 GGTCCTTTTCAGA 204AGAAAAAACTATTGATTC GAAACAGA ATCAATAGTTTT tp53 E5 12386T>TC,133M>M/TCCTGCCCTCAACAAGACGTTTT 115 TGTTCACTTGTGC 137 GCAGGTCTTGGCC 177GCCAACTGGCC CCTGACT AGTTG cdh1 E15 93059G>GA GCTCATCTCTAAGCTCAGGAAG 116CCAAAGCATGGCT 205 CTCAGGCAAGCTG 206 AGTTGTGTCAAAAATGAGA CATCTCTA AAAACATtp53 E8 13798G>GA:273R>R/H CGGAACAGCTTTGAGGTGCATG 117 CTACTGGGACGGA 146GCGGAGATTCTCT 182 TTTGTGCCTGTCCTGGG ACAGCTT TCCTCTGp53 E6, 12698_12701het_delAC TGGAAGGAAATTTGCGTGTGGA 118 GTGGAAGGAAATT136 AGCTGTTTGAGGA 172 (1 or 2 AC repeats) GTATTTGGATGACAG TGCGTGTGGTGGTG P53 E8, 13824C>CT, 282R>R/W TGTCCTGGGAGAGACTGGCGCA 119CTACTGGGACGGA 146 GCGGAGATTCTCT 182 CAGAGGAAGAGAAT ACAGCTT TCCTCTGAPC 102151G>GA, 1314R>R/R AAGAAAAGATTGGAACTAGATC 120 CAGACGACACAGG 133GTGACACTGCTGG 207 AGCTGAAGATCCTGTG AAGCAGA AACTTCG P53 E5 12457 G>G/TCGCCCGGCACCCGCTTCCGCGC 121 GTGCAGCTGTGGG 142 CTCCGTCATGTGC 170 CATGGCCATTGATT TGTGACT p53 E*13812C>CG,278P>P/A GTGTTTGTGCCTGTGCTGGGAG 122CTACTGGGACGGA 146 GCGGAGATTCTCT 182 AGACCGGCG ACAGCTT TCCTCTGAPC 102686het_delA AGGTTCTTCCAGATGCTGATAC 123 CTGCAGTTCAGAG 210CACTCAGGCTGGA  22 TTTATTACATTTTGC GGTCCAG TGAACAAAPC het_delAG between 102594_ GCGAGAAGTACCTAAAAATAAA 124 AGCTCAAACCAAG131 AGCATCTGGAAGA  28 102603 (1 of 5 AG repeats) GCACCTACTGCTGAA CGAGAAGACCTGGA APC 102240C>CA,1344S>S/X CAGGGTTCTAGTTTATCTTAAG 125CCCTAGAACCAAA 166 TGTCTGAGCACCA 208 AATCAGCCAGGCACA TCCAGCA CTTTTGG102676102680delACATT CCAGATGCTGATACTTTATTTT 126 CTGCAGTTCAGAG 167CACTCAGGCTGGA  22 GCCACGGAAAGTACTC GGTCCAG TGAACAA 12487C>CT:167Q>Q/XATGGCCATCTACAAGCAGTCAT  49 AGTACTCCCCTGC 128 CTCACAACCTCCG 169AGCACATGACGGAGGTTGT CCTCAAC TCATGTG 102447_102450het_delTGGTAGAGTGAACCATGCAGTGGAAA  50 TTTGAGAGTCGTT 129 CATGGTTTGTCCA  27AGTGGCATTATAAGCCC CGATTGC GGGCTAT 12410G>GA,141C>C/YTTTGCCAACTGGCCAAGACCTA  51 AGTACTCCCCTGC 128 CTCCGTCATGTGC 170CCCTGTGCAGCTGTG CCTCAAC TGTGACT 10267het_delA CAGATGCTGATACTTTATTACT  52TCCAGGTTCTTCC 130 CACTCAGGCTGGA  22 TTTGCCACGGAAAGTACT AGATGCT TGAACAA102594_102598het_delAGAGA AAAGCACCTACTGCTGAAAGAG 127 AGCTCAAACCAAG 131AGCATCTGGAAGA  28 AGTGGACCTAAGCAAG CGAGAAG ACCTGGA 102776A>AT:1523R>R/XATGACAATGGGAATGAAACAGA  14 TTTGCCACGGAAA 168 TTTCCTGAACTGG 209ATCAGAGCAGCCTAAAG GTACTCC AGGCATT 102644_102645het_insGATGCTGCAGTTCAGAGGGGTCC  54 GGACCTAAGCAAG 132 CACTCAGGCTGGA  22AGGTTCTTCCAGATGC CTGCAGTA TGAACAA 102594_102595het_delAGTAAAGCACCTACTGCTGAAAAG  55 AGCTCAAACCAAG 131 AGCATCTGGAAGA  28AGAGAGTGGACCTAAGCAAG CGAGAAG ACCTGGA 102106het_delTCACAGGAAGCAGATTCTGCAAT  56 CAGACGACACAGG 133 TGCTGGATTTGGT 171ACCCTGCAAATAGCA AAGCAGA TCTAGGG 102442het_delT TTCAGAGTGAACCATGCAGGGA 57 TTTGAGAGTCGTT 129 CATGGTTTGTCCA  27 ATGGTAAGTGGCATTAT CGATTGCGGGCTAT apc 102494C>CT;1429Q>Q/X TCCAGATAGCCCTGGATAAACC  58GTGAACCATGCAG  25 AGCTGTTTGAGGA 172 ATGCCACCAAG TGGAATG GGTGGTGapc 102140het_delA AGCAGAAATAAAAGAAAAGTTG  60 CAGACGACACAGG 133TGCTGGATTTGGT 171 GAACTAGGTCAGCTGA AAGCAGA TCTAGGG apc 102554het_delACTCAAACAGCTCAAACCAGCGA  61 CATGCCACCAAGC  21 GCAGCTTGCTTAG 173GAAGTACCTAAA AGAAGTA GTCCACT

Target mutations were not detected in control stools. Target mutationswere detected in stools from 68% (47/69) of patients with a GI neoplasm.Specifically, target mutations were detected in stools from 71% (36/51)of patients with cancer [40% (2/5) with oropharyngeal, 65% (11/17) withesophageal, 100% (4/4) with gastric, 55% (6/11) with pancreatic, 75%(3/4) with biliary or gallbladder, and 100% (10/10) with colorectal] andfrom 61%(11/18) with precancers [100% (2:2) with pancreaticintraductular papillary mucinous neoplasia and 56% (9/16) withcolorectal advanced adenoma]. Mutant copies in genes recovered fromstool averaged 0.4% (range 0.05-13.4%) for supracolonic and 1.4%(0.1-15.6%) for colorectal neoplasms, p=0.004 (Table 8).

TABLE 8 Digital Melt Curve Detection of Validated Mutations in AD CancerPatient Stool Stool Mutation Normal # ID Site Age Gender Tissue MutationDetection Frequency % Control 1 1163 Head/Neck(pharynx) 73 M tp53 YES0.8 Neg 2 1250 Head/Neck(pharynx) 49 M tp53 NO Neg 3 1295Head/Neck(pharynx) 47 F tp53 NO Neg 4 1391 Head/Neck 65 M tp53 TP53 NO(both) Neg 5 1427 Head/Neck 60 M tp53 tp53 YES(p53-1), 0.05 Neg No(p53-2) 1 745 Esophagus 84 F tp53 YES 0.4 Neg 2 769 Esophagus 56 F tp53YES 0.4 Neg 3 782 Esophagus 55 M tp53 NO Neg 4 789 Esophagus 61 M tp53YES 1.6 Neg 5 819 Esophagus 53 M tp53 YES 0.2 Neg 6 873 Esophagus 61 Mtp53 YES 0.2 Neg 7 906 Esophagus 55 M APC YES 0.8 Neg 8 1049 Esophagus57 M tp53 NO Neg 9 1064 Esophagus 72 F tp53 NO Neg 10 1067 Esophagus 72M tp53 YES 0.7 Neg 11 1103 Esophagus 78 M tp53 NO Neg 12 1199 Esophagus66 M tp53 YES 0.5 NEG 13 1307 Esophagus 51 M tp53 NO NEG 14 1373Esophagus 76 M tp53 YES 0.5 NEG 15 1414 Esophagus 66 M tp53 YES 0.1 NEG16 1448 Esophagus 82 M tp53 NO NEG 17 1072 Esophagus tp53 YES 0.4 NEG 1798 Stomach 81 M cdh1 YES 13.2 NEG 3 1221 Stomach 55 M cdh1 cdh1YES(both)   8, 1.3 NEG 4 1224 Stomach 75 F smad4 cdh1 YES(smad4), 0.2NEG No(CDH1) 5 1402 Stomach 56 M APC tp53 YES (p53) 0.1 NEG 1 848 GallBladder 67 M tp53 YES 0.1 NEG 2 1315 Gall Bladder 57 F tp53 YES 1.4 NEG1 1043 Bile Duct 51 F APC NO NEG 2 1554 Bile Duct 77 M cdh1 YES 13.4 NEG1 757 Pancreatic Cancer 78 M K-ras YES 0.2 NEG in situ 2 1349 PancreaticCancer 64 M K-ras YES 0.2 NEG in situ 1 839 Pancreas 69 F tp53 YES 0.2NEG 2 1204 Pancreas 65 F p16 NO NEG 3 1253 Pancreas 63 F K-ras tp53Yes(k-ras), 2 NEG No(p53) 4 1400 Pancreas 71 F tp53 K-ras No(both) NEG 51547 Pancreas 77 F tp53 NO NEG 6 1217 Pancreas K-ras NO NEG 7 1073Pancreas K-ras NO NEG 8 532 Pancreas K-ras YES 1 NEG 9 1592 PancreasK-ras P53 YES (both) 0.3 NEG 10 1695 Pancreas K-ras YES 0.2 NEG 11 1058Pancreas K-ras P53 APC YES(K-ras) 0.2 NEG 1 438 Colorectal 78 F APC YES1.2 NEG Cancer 2 446 Colorectal 74 M BRAF YES 0.4 NEG Cancer 3 529Colorectal 46 M K-RAS YES 1 NEG Cancer 4 489 Colorectal 73 M K-RAS YES2.6 NEG Cancer 5 549 Colorectal 79 M BRAF YES 1.6 NEG Cancer 6 551Colorectal 69 M K-RAS YES 5.8 NEG Cancer 7 584 Colorectal 68 M K-RAS YES1.4 NEG Cancer 8 894 Colorectal 57 M P53 APC YES(p53, 1.6, 5   NEGCancer APC) 9 998 Colorectal 45 F APC KRAS YES(K-ras, 0.6, 0.8 NEGCancer APC) 10 1009 Colorectal 65 F P53 YES 12.9 NEG Adenoma 1 513Colorectal 65 F APC YES 0.1 NEG Adenoma 2 546 Colorectal 61 M APC NO NEGAdenoma 3 547 Colorectal 52 F APC NO NEG Adenoma 4 568 Colorectal 52 MAPC YES 7.8 NEG Adenoma 5 578 Colorectal 71 F APC NO NEG Adenoma 6 590Colorectal 54 F APC YES 3.2 NEG Adenoma 7 701 Colorectal 72 F APC NO NEGAdenoma 8 855 Colorectal 75 M K-RAS YES 0.4 NEG Adenoma 9 860 Colorectal53 M APC YES 15.6 NEG Adenoma 10 900 Colorectal 64 F APC K-RAS No(both)NEG Adenoma 11 962 Colorectal 56 M K-RAS Yes 1 NEG Adenoma 12 965Colorectal 82 M APC K-RAS No (both) NEG Adenoma 13 991 Colorectal 79 MAPC K-RAS YES(K-ras), 0.2 NEG Adenoma No(APC) 14 1135 Colorectal 59 MK-RAS YES 13 NEG Adenoma 15 1231 Colorectal 50 M APC NO NEG Adenoma 161559 Colorectal K-RAS YES 1 NEG Adenoma

We also performed an initial pilot study with 10 stool samples frompatients with confirmed bile duct cancers to determine if DMC technologycould detect mutations in k-ras, a well characterized gene known bemutated in this population. K-ras mutations were detected in stools for3/10 or 4/10 bile duct cancers (depending on mutation score of 5 or 3,respectively) (Table 9). As K-ras is mutant in 30-40% of bile ductcancers, these results indicate that the detection assay is picking upthe appropriate proportion of cancer samples.

TABLE 9 K-ras mutation scores for patients with bile duct cancer. K-rasMutation Mutation Detected Sample # Pathology Score A B 520 BD Cancer 0528 BD Cancer 0 559 BD Cancer 0 558 BD Cancer 1 Codon 12 GAT 515 BDCancer 2 Codon 12 GAT 543 BD Cancer 2 Codon 13 GAC 806 BD Cancer 3 Codon12 TGT 539 BD Cancer 5 Codon 12 GAT Codon 13 GGA 512 BD Cancer 6 Codon12 GAT Codon 12 GAT 725 BD Cancer 25 Codon 13 GAC Codon 12 GAT; Codon 12GTT

Allele Specific PCR

The allele specific-PCR assay was a modified version of a previouslypublished method (e.g., Cha et al., Mismatch Amplification MutationAssay (MAMA): Application to the c-H-ras Gene PCR Methods andApplications, 2: 14-20 (1992) Cold Spring Harbor Laboratory). TP53 genefragments were captured from stool DNA samples with probes specific tomutations identified in the matched tissue (Table 7). Copy numbers wereassessed by qPCR. Samples were adjusted to 10,000 fragments each andamplified with allele specific primer sets

Sample F Primer R Primer A745 GACAGAAACACTTTAT CGGCTCATAGGG(SEQ ID No: 211) (SEQ ID NO: 217) A848 ACACTTTTCGACAAG AAACCAGACCTCAG(SEQ ID No: 212) (SEQ ID No: 218) A789 CCTCAACAAGATAC CAGCTGCACAGG(SEQ ID No: 213) (SEQ ID NO: 219) A782 GCCGCCTGAAA AGACCCCAGTTGC(SEQ ID No: 214) (SEQ ID No: 220) A873 GCGGCATGAAAT TTCCAGTGTGATGAT(SEQ ID No: 215) (SEQ ID NO: 221) A769 CCCCTCCTCAGAG CTTCCACTCGGATAA(SEQ ID No: 216) (SEQ ID No: 222)

The Forward Primer in Each Case is Specific for Each TP53 Mutation.Esophagus and Stomach

Targeting mutations found in esophageal cancers or those fromgastroesophageal junction (on p53, APC, or K-ras), the same mutation wasdetected by allele-specific PCR in matched stools from five of five(100%) cancers but in none of the controls (Table 10). The thresholdcycle (Ct), designates the PCR cycle at which the product enters theexponential phase of amplification.

Gallbladder

Targeting a mutation confirmed in a gallbladder cancer, the samemutation was found in the matched stool from that patient usingallele-specific PCR (Table 10).

TABLE 10 Quantitative Mutant Allele Specific-PCR Results for MatchedAero- digestive Cancers Sample Gene # fragments Ct A769esophageal/gastric cancer p53 10K 71 N normal p53 10K >80 A782esophageal/gastric cancer p53 10K 38.8 N normal p53 10K 44.1 A745esophageal/gastric cancer p53 30K 42.5 N normal p53 30K 45.6 A873esophageal/gastric cancer p53 30K 37.8 N normal p53 30K 40.4 A848 gallbladder cancer p53 10K 22.4 N normal p53 10K 36.3 A789esophageal/gastric cancer p53 10K 25.9 N normal p53 10K 28.7

Example 9 Candidate Stool Polypeptide Markers Identified for ColorectalCancer and Precancerous Adenomas

The following list of polypeptides were identified by a statisticalanalysis model using all data generated from mass spectral of fecalprotein extracts: β2-macroglobulin, compliment C3 protein,serotransferrin, haptoglobin, carbonic anhydrase 1, xaa-pro dipeptidase,leukocyte elastase inhibitor, hemoglobin, glucose-6-phosphate, andcatalase. This list of polypeptides is in order of difference fromnormal. Thus, the mean spectral abundance for β2-macroglobulin is mostdifferent from normal for cancer and adenoma.

The statistical significance of relative poly peptide abundance betweennormal, adenoma, and colorectal cancer (CRC) was obtained usingnormalized spectral count data from a zero inflated poisson regressionmodel as an offset term in the protein specific differential expressionanalysis. The differential expression analysis also incorporated thezero inflated poisson regression model. Polypeptides were then rankedaccording to their statistical significance and whether the expressionprofile followed the clinically relevant pattern of Normal <Adenoma<CRC. Using a rule that a positive test required that any three of topsix markers be positive, the sensitivity and specificity of this panelwere both 100% in a training set.

The listed polypeptides can be used individually or in any combinationto detect colorectal cancer or precancerous adenomas.

Example 10 Identification of Polypeptide Markers for Pancreatic Cancer

Potential polypeptide markers for pancreatic cancer prediction wereidentified. Utilizing a Scaffold (Proteome Software) side-by-sidecomparison of spectral abundances, ratios of the spectral counts ofcarboxypeptidase B (CBPB1_HUMAN) and Carboxypeptidase A1 (CBPA1_HUMAN)were compared. A value for carboxypeptidase B/Al of 0.7 or higher waspredictive of pancreatic cancer (m normal stools, an average ratio of2:3 B/Al was observed). Specificity for training data was 100% with asensitivity of 88%, while sensitivity from a validation set was 82% atthe same specificity.

Example 11 Use of Fecal Methylated ALX4 as a Neoplasia Marker

Stools from patients with colorectal tumors were found to containsignificantly elevated amounts of methylated ALX4 gene copies, but thosefrom normal individuals were found to contain none or only traceamounts. When fecal methylated ALX4 was assayed with an appropriateamplification method, colorectal cancers and premalignant adenomas werespecifically detected. At 90% specificity, fecal methylated ALX4detected 59% colorectal cancer and 54% premalignant adenomas, allowingfor the detection of both colorectal cancer and premalignant adenomas.

Other Embodiments

It is to be understood that while the invention has been described inconjunction with the detailed description thereof, the foregoingdescription is intended to illustrate and not limit the scope of theinvention, which is defined by the scope of the appended claims. Otheraspects, advantages, and modifications are within the scope of thefollowing claims.

What is claimed is:
 1. A method of detecting pancreatic cancer in amammal, wherein said method comprises determining the ratio of anelastase 3 A polypeptide to a pancreatic alpha-amylase polypeptidepresent within a stool sample, wherein the presence of a ratio greaterthan about 0.5 indicates that said mammal has said pancreatic cancer,and wherein the presence of a ratio less than about 0.5 indicates thatsaid mammal does not have said pancreatic cancer.